Thermodynamic Integration with Enhanced Sampling (TIES)

Similar documents
Lecture 19: Free Energies in Modern Computational Statistical Thermodynamics: FEP and Related Methods

Large Scale FEP on Congeneric Ligand Series Have Practical Free Energy Calculations arrived at Last?

In silico pharmacology for drug discovery

Computational chemical biology to address non-traditional drug targets. John Karanicolas

Molecular Interactions F14NMI. Lecture 4: worked answers to practice questions

MM-GBSA for Calculating Binding Affinity A rank-ordering study for the lead optimization of Fxa and COX-2 inhibitors

Early Stages of Drug Discovery in the Pharmaceutical Industry

MD Simulation in Pose Refinement and Scoring Using AMBER Workflows

Structure based drug design and LIE models for GPCRs

Machine-learning scoring functions for docking

BUDE. A General Purpose Molecular Docking Program Using OpenCL. Richard B Sessions

New approaches to scoring function design for protein-ligand binding affinities. Richard A. Friesner Columbia University

Dr. Sander B. Nabuurs. Computational Drug Discovery group Center for Molecular and Biomolecular Informatics Radboud University Medical Centre

CSD. CSD-Enterprise. Access the CSD and ALL CCDC application software

The PhilOEsophy. There are only two fundamental molecular descriptors

Receptor Based Drug Design (1)

Alchemical free energy calculations in OpenMM

Retrieving hits through in silico screening and expert assessment M. N. Drwal a,b and R. Griffith a

Docking. GBCB 5874: Problem Solving in GBCB

High Throughput In-Silico Screening Against Flexible Protein Receptors

11. IN SILICO DOCKING ANALYSIS

FRAGMENT SCREENING IN LEAD DISCOVERY BY WEAK AFFINITY CHROMATOGRAPHY (WAC )

GC and CELPP: Workflows and Insights

Structural Bioinformatics (C3210) Molecular Docking

Kd = koff/kon = [R][L]/[RL]

Free energy calculations with alchemlyb

Bridging the Dimensions:

Softwares for Molecular Docking. Lokesh P. Tripathi NCBS 17 December 2007

D3RGC2: free energy scoring by alchemical free energy implementation in SOMD

STRUCTURAL BIOINFORMATICS II. Spring 2018

Cross Discipline Analysis made possible with Data Pipelining. J.R. Tozer SciTegic

Progress of Compound Library Design Using In-silico Approach for Collaborative Drug Discovery

Hit Finding and Optimization Using BLAZE & FORGE

Pose and affinity prediction by ICM in D3R GC3. Max Totrov Molsoft

Statistical Mechanics for Proteins

Structural biology and drug design: An overview

Artificial Intelligence Methods for Molecular Property Prediction. Evan N. Feinberg Pande Lab

Free energy simulations

CHEM-UA 652: Thermodynamics and Kinetics

Protein-Ligand Docking Evaluations

Using Phase for Pharmacophore Modelling. 5th European Life Science Bootcamp March, 2017

A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery

Fondamenti di Chimica Farmaceutica. Computer Chemistry in Drug Research: Introduction

Virtual Screening: How Are We Doing?

Microcalorimetry for the Life Sciences

Biomolecular modeling. Theoretical Chemistry, TU Braunschweig (Dated: December 10, 2010)

Binding Free Energy Calculations for Lead Optimization: Assessment of Their Accuracy in an Industrial Drug Design Context

Principles of Drug Design

Structure-based maximal affinity model predicts small-molecule druggability

Molecular Modelling for Medicinal Chemistry (F13MMM) Room A36

Using Bayesian Statistics to Predict Water Affinity and Behavior in Protein Binding Sites. J. Andrew Surface

Topology based deep learning for biomolecular data

Dock Ligands from a 2D Molecule Sketch

Presentation Microcalorimetry for Life Science Research

est Drive K20 GPUs! Experience The Acceleration Run Computational Chemistry Codes on Tesla K20 GPU today

Advanced in silico drug design

Implementation of novel tools to facilitate fragment-based drug discovery by NMR:

Introduction to FBDD Fragment screening methods and library design

A Large-Scale Test of Free-Energy Simulation Estimates of Protein-Ligand Binding Affinities.

Virtual screening for drug discovery. Markus Lill Purdue University

Data Quality Issues That Can Impact Drug Discovery

October 6 University Faculty of pharmacy Computer Aided Drug Design Unit

Problem Set 5 Question 1

Using AutoDock for Virtual Screening

MM-PBSA Validation Study. Trent E. Balius Department of Applied Mathematics and Statistics AMS

Cheminformatics platform for drug discovery application

Scoring functions for of protein-ligand docking: New routes towards old goals

Using Design of Experiments to Optimize Chiral Separation

Ligand Scout Tutorials

Protein Structure Prediction and Protein-Ligand Docking

Enhancing Specificity in the Janus Kinases: A Study on the Thienopyridine. JAK2 Selective Mechanism Combined Molecular Dynamics Simulation

My Career in the Pharmaceutical Industry

Next Generation Computational Chemistry Tools to Predict Toxicity of CWAs

A COMPARATIVE STUDY OF MACHINE-LEARNING-BASED SCORING FUNCTIONS IN PREDICTING PROTEIN-LIGAND BINDING AFFINITY. Hossam Mohamed Farg Ashtawy A THESIS

Lots of thanks for many reasons!

Biologically Relevant Molecular Comparisons. Mark Mackey

Molecular Modelling. Computational Chemistry Demystified. RSC Publishing. Interprobe Chemical Services, Lenzie, Kirkintilloch, Glasgow, UK

Zoe Blaxill Analytical Sciences Discovery Research. High Frequency Acoustic Technology: Evaluation for Compound Mixing and Dissolution in HTS.

The Quantum Landscape

Structure Investigation of Fam20C, a Golgi Casein Kinase

Investigation of physiochemical interactions in

Free energy calculations

Practical QSAR and Library Design: Advanced tools for research teams

Quantitative Structure-Activity Relationship (QSAR) computational-drug-design.html

OpenDiscovery: Automated Docking of Ligands to Proteins and Molecular Simulation

Towards Physics-based Models for ADME/Tox. Tyler Day

MOLECULAR RECOGNITION DOCKING ALGORITHMS. Natasja Brooijmans 1 and Irwin D. Kuntz 2

Computational Chemistry in Drug Discovery. Darren Green EBI 2016

FRAUNHOFER IME SCREENINGPORT

Ranking of HIV-protease inhibitors using AutoDock

QM/MM study on inhibitor of HIV-1 protease. Graduate School of Science, Kyoto University Masahiko Taguchi, Masahiko Kaneso, Shigehiko Hayashi

DOCKING TUTORIAL. A. The docking Workflow

Protein Simulations in Confined Environments

Introduction to Chemoinformatics and Drug Discovery

SAMPL6 pka Challenge

Dispensing Processes Profoundly Impact Biological, Computational and Statistical Analyses

Binding Response: A Descriptor for Selecting Ligand Binding Site on Protein Surfaces

COMBINATORIAL CHEMISTRY: CURRENT APPROACH

Joana Pereira Lamzin Group EMBL Hamburg, Germany. Small molecules How to identify and build them (with ARP/wARP)

Isothermal Titration Calorimetry in Drug Discovery. Geoff Holdgate Structure & Biophysics, Discovery Sciences, AstraZeneca October 2017

Transcription:

Thermodynamic Integration with Enhanced Sampling (TIES) A. P. Bhati, S. Wan, D. W. Wright and P. V. Coveney agastya.bhati.14@ucl.ac.uk 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

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 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 16 th 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 16 th 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 16 th 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 16 th 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 16 th May 2017 3 21

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 2017 4 21

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, 30236-30240, DOI: 10.1039/C6CP02349E Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 4 21

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, 30236-30240, DOI: 10.1039/C6CP02349E Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 4 21

Binding Affinity Agastya P. Bhati (CCS, UCL) TIES 16 th 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 binding Agastya P. Bhati (CCS, UCL) TIES 16 th 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 16 th May 2017 6 21

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 2017 7 21

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 2017 8 21

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 2017 8 21

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 2017 8 21

Application of binding affinity prediction Drug designing: Lead optimisation Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 9 21

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 2017 9 21

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 2017 9 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 2017 9 21

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 2017 9 21

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 2017 10 21

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 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 CDK2 2 Drug-HIV1 protease 3 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: 10.1021/acs.jctc.6b00979 2 Wright, Hall, Kenway, Jha & Coveney, JCTC, (2014), DOI: 10.1021/ct4007037 Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 11 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 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: 10.1021/acs.jctc.6b00979 2 Wright, Hall, Kenway, Jha & Coveney, JCTC, (2014), DOI: 10.1021/ct4007037 Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 11 21

Thermodynamic Integration with Enhanced Sampling (TIES) 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: 10.1021/acs.jctc.6b00979 Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 12 21

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: 10.1021/acs.jctc.6b00979 Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 12 21

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: 10.1021/acs.jctc.6b00979 Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 13 21

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 2017 14 21

TIES predictions 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: 10.1021/acs.jctc.6b00979 Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 15 21

TIES predictions 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: 10.1021/acs.jctc.6b00979 Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 15 21

TIES reproducibility Reproducibility of TIES predictions for transformation between ligands bound to CDK2 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: 10.1021/acs.jctc.6b00979 Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 16 21

Thrombin S1 pocket: water inflow Captured successfully by TIES 1 Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: 10.1021/acs.jctc.6b00979 Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 17 21

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 0.88 10 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: 10.1021/acs.jcim.6b00780 Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 18 21

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 0.88 10 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: 10.1021/acs.jcim.6b00780 Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 18 21

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 0.88 10 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: 10.1021/acs.jcim.6b00780 Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 18 21

Blind study with GSK: Bromodomain 4 1 Wan, Bhati, Zasada, Wall, Green, Bamborough, Coveney, JCTC (2017), DOI: 10.1021/acs.jctc.6b00794 Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 19 21

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 2017 20 21

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 2017 21 21

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 2017 21 21

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 2017 21 21

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 2017 21 21

2024 © sciencedocbox.com Privacy Policy | Terms of Service | Feedback