Thermodynamic Integration with Enhanced Sampling (TIES)
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1 Thermodynamic Integration with Enhanced Sampling (TIES) A. P. Bhati, S. Wan, D. W. Wright and P. V. Coveney Centre for Computational Science Department of Chemistry University College London 16 th May 2017 Agastya P. Bhati (CCS, UCL) TIES 16 th May
2 Credits due Peter V. Coveney Shunzhou Wan David W. Wright UCL Overseas Research Scholarship Inlaks Shivdasani Foundation Agastya P. Bhati (CCS, UCL) TIES 16 th May
3 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 16 th May
4 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 16 th May
5 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 16 th May
6 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 16 th May
7 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 16 th May
8 Molecular Dynamics (MD) Simulations An experiment is usually made on a macroscopic sample that contains an extremely large number of atoms or molecules sampling an enormous number of conformations. 1 Agastya P. Bhati (CCS, UCL) TIES 16 th May
9 Molecular Dynamics (MD) Simulations An experiment is usually made on a macroscopic sample that contains an extremely large number of atoms or molecules sampling an enormous number of conformations. In MD simulation, macroscopic properties corresponding to experimental observables are defined in terms of ensemble averages. 1 1 Coveney & Wan, PCCP, 2016, 18, , DOI: /C6CP02349E Agastya P. Bhati (CCS, UCL) TIES 16 th May
10 Molecular Dynamics (MD) Simulations An experiment is usually made on a macroscopic sample that contains an extremely large number of atoms or molecules sampling an enormous number of conformations. In MD simulation, macroscopic properties corresponding to experimental observables are defined in terms of ensemble averages. 1 Free energy is such a measurement 1 Coveney & Wan, PCCP, 2016, 18, , DOI: /C6CP02349E Agastya P. Bhati (CCS, UCL) TIES 16 th May
11 Binding Affinity Agastya P. Bhati (CCS, UCL) TIES 16 th May
12 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 16 th May
13 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 16 th May
14 Thermodynamic Integration Hybrid potential function: H(λ) = λh A + (1 λ)h B The coupling parameter, λ, defines the progress of a system along the path, B to A, as λ is changed from 0 to 1. G = 1 H(λ) dλ λ λ 0 Alchemical mutation from B (left) to A (right) Agastya P. Bhati (CCS, UCL) TIES 16 th May
15 Relative Binding Affinity Relative binding affinity calculations with alchemical mutation: make use of thermodynamic cycle to calculate binding free energy differences Agastya P. Bhati (CCS, UCL) TIES 16 th May
16 Relative Binding Affinity Relative binding affinity calculations with alchemical mutation: make use of thermodynamic cycle to calculate binding free energy differences Agastya P. Bhati (CCS, UCL) TIES 16 th May
17 Relative Binding Affinity Relative binding affinity calculations with alchemical mutation: make use of thermodynamic cycle to calculate binding free energy differences G binding = G binding ligand2 Gbinding ligand1 = Galch ligand Galch complex Agastya P. Bhati (CCS, UCL) TIES 16 th May
18 Application of binding affinity prediction Drug designing: Lead optimisation Agastya P. Bhati (CCS, UCL) TIES 16 th May
19 Application of binding affinity prediction Drug designing: Lead optimisation Searching for a needle in a haystack Agastya P. Bhati (CCS, UCL) TIES 16 th May
20 Application of binding affinity prediction Drug designing: Lead optimisation Searching for a needle in a haystack Agastya P. Bhati (CCS, UCL) TIES 16 th May
21 Application of binding affinity prediction Drug designing: Lead optimisation Searching for a needle in a haystack High-Throughput Screening (HTS) HTS can test thousands of compounds per day Cost of HTS is substantial: $1-10 per compound Agastya P. Bhati (CCS, UCL) TIES 16 th May
22 Application of binding affinity prediction Drug designing: Lead optimisation Searching for a needle in a haystack Virtual Screening: Systematic computer-based prediction of binding affinity of compounds to proteins Agastya P. Bhati (CCS, UCL) TIES 16 th May
23 Issues with the available in silico methods Theories exist - Then predictions are possible, and in principle, we should be able to apply existing methods in drug screening domains Agastya P. Bhati (CCS, UCL) TIES 16 th May
24 Issues with the available in silico methods Theories exist - Then predictions are possible, and in principle, we should be able to apply existing methods in drug screening domains Agastya P. Bhati (CCS, UCL) TIES 16 th May
25 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 CDK2 2 Drug-HIV1 protease 3 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: /acs.jctc.6b Wright, Hall, Kenway, Jha & Coveney, JCTC, (2014), DOI: /ct Agastya P. Bhati (CCS, UCL) TIES 16 th May
26 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 CDK2 2 Drug-HIV1 protease 3 The energy/energy derivatives from ensemble simulations follow well defined Gaussian distributions. 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: /acs.jctc.6b Wright, Hall, Kenway, Jha & Coveney, JCTC, (2014), DOI: /ct Agastya P. Bhati (CCS, UCL) TIES 16 th May
27 Thermodynamic Integration with Enhanced Sampling (TIES) 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: /acs.jctc.6b00979 Agastya P. Bhati (CCS, UCL) TIES 16 th May
28 Thermodynamic Integration with Enhanced Sampling (TIES) Binding Affinity Calculator (BAC) is a software toolkit which automates the implementation of TIES (and ESMACS) methods for binding affinity calculations 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: /acs.jctc.6b00979 Agastya P. Bhati (CCS, UCL) TIES 16 th May
29 TIES: Convergence of errors Variation of error with different parameters L1Q-LI9 ligand transformation bound to CDK2 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: /acs.jctc.6b00979 Agastya P. Bhati (CCS, UCL) TIES 16 th May
30 TIES: Biomolecular systems studied Crystal structures of the protein from the Protein Data Bank CDK2 MCL1 PTP1B Thrombin TYK2 Agastya P. Bhati (CCS, UCL) TIES 16 th May
31 TIES predictions 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: /acs.jctc.6b00979 Agastya P. Bhati (CCS, UCL) TIES 16 th May
32 TIES predictions 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: /acs.jctc.6b00979 Agastya P. Bhati (CCS, UCL) TIES 16 th May
33 TIES reproducibility Reproducibility of TIES predictions for transformation between ligands bound to CDK2 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: /acs.jctc.6b00979 Agastya P. Bhati (CCS, UCL) TIES 16 th May
34 Thrombin S1 pocket: water inflow Captured successfully by TIES 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: /acs.jctc.6b00979 Agastya P. Bhati (CCS, UCL) TIES 16 th May
35 Blind study with Pfizer: Tropomysin receptor kinase A The TIES study gives the same ranking of the binding free energies as the experimental data for the compounds studied Pearson correlation of out of the 14 pairs directional agreements A better agreement might be achieved when the error bars of the experimental G are also taken into account 1 Wan, Bhati, Skerratt, Omoto, Shanmugasundaram, Bagal, Coveney, JCIM (2017), DOI: /acs.jcim.6b00780 Agastya P. Bhati (CCS, UCL) TIES 16 th May
36 Blind study with Pfizer: Tropomysin receptor kinase A The TIES study gives the same ranking of the binding free energies as the experimental data for the compounds studied Pearson correlation of out of the 14 pairs directional agreements A better agreement might be achieved when the error bars of the experimental G are also taken into account 1 Wan, Bhati, Skerratt, Omoto, Shanmugasundaram, Bagal, Coveney, JCIM (2017), DOI: /acs.jcim.6b00780 Agastya P. Bhati (CCS, UCL) TIES 16 th May
37 Blind study with Pfizer: Tropomysin receptor kinase A The TIES study gives the same ranking of the binding free energies as the experimental data for the compounds studied Pearson correlation of out of the 14 pairs directional agreements A better agreement might be achieved when the error bars of the experimental G are also taken into account 1 Wan, Bhati, Skerratt, Omoto, Shanmugasundaram, Bagal, Coveney, JCIM (2017), DOI: /acs.jcim.6b00780 Agastya P. Bhati (CCS, UCL) TIES 16 th May
38 Blind study with GSK: Bromodomain 4 1 Wan, Bhati, Zasada, Wall, Green, Bamborough, Coveney, JCTC (2017), DOI: /acs.jctc.6b00794 Agastya P. Bhati (CCS, UCL) TIES 16 th May
39 Excellent scalability TIES workflow is perfectly scalable Giant run on PRACE s Tier0 SuperMUC LRZ press release London Science Museum blog post Agastya P. Bhati (CCS, UCL) TIES 16 th May
40 Conclusions The traditional in silico methods to calculate binding affinity are not reliable, and hence, not widely applicable in pharmaceutical domain Agastya P. Bhati (CCS, UCL) TIES 16 th May
41 Conclusions The traditional in silico methods to calculate binding affinity are not reliable, and hence, not widely applicable in pharmaceutical domain Ensemble simulation based method is the way out Agastya P. Bhati (CCS, UCL) TIES 16 th May
42 Conclusions The traditional in silico methods to calculate binding affinity are not reliable, and hence, not widely applicable in pharmaceutical domain Ensemble simulation based method is the way out TIES substantially improves the accuracy, precision and reliability of calculated relative binding affinities Agastya P. Bhati (CCS, UCL) TIES 16 th May
43 Conclusions The traditional in silico methods to calculate binding affinity are not reliable, and hence, not widely applicable in pharmaceutical domain Ensemble simulation based method is the way out TIES substantially improves the accuracy, precision and reliability of calculated relative binding affinities Ligand-protein binding affinity predictions made with TIES are reproducible Agastya P. Bhati (CCS, UCL) TIES 16 th May
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