Effect of set up protocols on the accuracy of alchemical free energy calculation over a set of ACK1 inhibitors

Hit-to-lead virtual screening frequently relies on a cascade of computational methods that starts with rapid calculations applied to a large number of compounds and ends with more expensive computations restricted to a subset of compounds that passed initial filters. This work focuses on set up protocols for alchemical free energy (AFE) scoring in the context of a Docking / MM/PBSA / AFE cascade. A dataset of 15 congeneric inhibitors of the ACK1 protein was used to evaluate the performance of AFE set up protocols that varied in the steps taken to prepare input files (using previously docked and best scored poses, manual selection of poses, manual placement of binding site water molecules). The main finding is that use of knowledge derived from X-ray structures to model binding modes, together with the manual placement of a bridging water molecule, improves the R2 from 0.45 +/- 0.06 to 0.76 +/- 0.02 and decreases the mean unsigned error from 2.11 +/- 0.08 to 1.24 +/- 0.04 kcal mol-1. By contrast a brute force automated protocol that increased the sampling time ten-fold lead to little improvements in accuracy. Besides, it is shown that for the present dataset hysteresis can be used to flag poses that need further attention even without prior knowledge of experimental binding affinities.

[1]  Shuai Liu,et al.  D3R Grand Challenge 2: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies , 2017, Journal of Computer-Aided Molecular Design.

[2]  Samuel Genheden,et al.  A Large-Scale Test of Free-Energy Simulation Estimates of Protein-Ligand Binding Affinities , 2014, J. Chem. Inf. Model..

[3]  Araz Jakalian,et al.  Fast, efficient generation of high‐quality atomic charges. AM1‐BCC model: I. Method , 2000 .

[4]  Christopher I. Bayly,et al.  Fast, efficient generation of high‐quality atomic charges. AM1‐BCC model: II. Parameterization and validation , 2002, J. Comput. Chem..

[5]  J. Rubio-Martínez,et al.  A multistep docking and scoring protocol for congeneric series: Implementation on kinase DFG-out type II inhibitors. , 2018, Future medicinal chemistry.

[6]  Julien Michel,et al.  Blinded predictions of host-guest standard free energies of binding in the SAMPL5 challenge , 2016, Journal of Computer-Aided Molecular Design.

[7]  Gerhard König,et al.  Multiscale Free Energy Simulations: An Efficient Method for Connecting Classical MD Simulations to QM or QM/MM Free Energies Using Non-Boltzmann Bennett Reweighting Schemes , 2014, Journal of chemical theory and computation.

[8]  Julien Michel,et al.  Prediction of the water content in protein binding sites. , 2009, The journal of physical chemistry. B.

[9]  J. Åqvist,et al.  Molecular Dynamics Simulations of Water and Biomolecules with a Monte Carlo Constant Pressure Algorithm , 2004 .

[10]  Julien Michel,et al.  Evaluation of Host-Guest Binding Thermodynamics of Model Cavities with Grid Cell Theory. , 2014, Journal of chemical theory and computation.

[11]  Jaroslav Koča,et al.  Evaluation of Selected Classical Force Fields for Alchemical Binding Free Energy Calculations of Protein-Carbohydrate Complexes. , 2015, Journal of chemical theory and computation.

[12]  Hannes H. Loeffler,et al.  FESetup: Automating Setup for Alchemical Free Energy Simulations , 2015, J. Chem. Inf. Model..

[13]  Christina Athanasiou,et al.  Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2 , 2017, Journal of Computer-Aided Molecular Design.

[14]  K. Gajiwala,et al.  Ack1: Activation and Regulation by Allostery , 2013, PloS one.

[15]  K Schulten,et al.  VMD: visual molecular dynamics. , 1996, Journal of molecular graphics.

[16]  Nicolas Foloppe,et al.  Rigorous Free Energy Calculations in Structure‐Based Drug Design , 2010, Molecular informatics.

[17]  Jonathan W. Essex,et al.  Strategies to Calculate Water Binding Free Energies in Protein-Ligand Complexes , 2014, J. Chem. Inf. Model..

[18]  D. Ferguson,et al.  Isothermal-isobaric molecular dynamics simulations with Monte Carlo volume sampling , 1995 .

[19]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..

[20]  P. Kollman,et al.  Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. , 2000, Accounts of chemical research.

[21]  Akshay Sridhar,et al.  Waterdock 2.0: Water placement prediction for Holo-structures with a pymol plugin , 2017, PloS one.

[22]  Bryce K. Allen,et al.  Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations , 2017, J. Chem. Inf. Model..

[23]  Elizabeth Yuriev,et al.  Free Energy Methods in Drug Design: Prospects of "Alchemical Perturbation" in Medicinal Chemistry. , 2017, Journal of medicinal chemistry.

[24]  V. Hornak,et al.  Comparison of multiple Amber force fields and development of improved protein backbone parameters , 2006, Proteins.

[25]  Demetri T. Moustakas,et al.  Evaluating Free Energies of Binding and Conservation of Crystallographic Waters Using SZMAP , 2015, J. Chem. Inf. Model..

[26]  C. Simmerling,et al.  ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. , 2015, Journal of chemical theory and computation.

[27]  Scott P. Brown,et al.  Healthy skepticism: assessing realistic model performance. , 2009, Drug discovery today.

[28]  Baofeng Zhang,et al.  Large scale free energy calculations for blind predictions of protein–ligand binding: the D3R Grand Challenge 2015 , 2016, Journal of Computer-Aided Molecular Design.

[29]  Markus A. Lill,et al.  WATsite: Hydration site prediction program with PyMOL interface , 2014, J. Comput. Chem..

[30]  T. Darden,et al.  Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems , 1993 .

[31]  Julien Michel,et al.  Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R grand challenge. , 2016, Bioorganic & medicinal chemistry.

[32]  Julien Michel,et al.  Elucidation of Nonadditive Effects in Protein-Ligand Binding Energies: Thrombin as a Case Study. , 2016, The journal of physical chemistry. B.

[33]  K N Houk,et al.  Beyond picomolar affinities: quantitative aspects of noncovalent and covalent binding of drugs to proteins. , 2009, Journal of medicinal chemistry.

[34]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .

[35]  K. Mahajan,et al.  Shepherding AKT and androgen receptor by Ack1 tyrosine kinase , 2010, Journal of cellular physiology.

[36]  Fumio Hirata,et al.  Placevent: An algorithm for prediction of explicit solvent atom distribution—Application to HIV‐1 protease and F‐ATP synthase , 2012, J. Comput. Chem..

[37]  Jonathan W. Essex,et al.  Prediction of protein–ligand binding affinity by free energy simulations: assumptions, pitfalls and expectations , 2010, J. Comput. Aided Mol. Des..

[38]  Themis Lazaridis,et al.  Inhomogeneous Fluid Approach to Solvation Thermodynamics. 2. Applications to Simple Fluids , 1998 .

[39]  T. Lazaridis Inhomogeneous Fluid Approach to Solvation Thermodynamics. 1. Theory , 1998 .

[40]  Jürgen Bajorath,et al.  Advances in Computational Medicinal Chemistry: A Reflection on the Evolution of the Field and Perspective Going Forward. , 2016, Journal of medicinal chemistry.

[41]  Holger Gohlke,et al.  MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. , 2012, Journal of chemical theory and computation.

[42]  Michael K. Gilson,et al.  Solvation thermodynamic mapping of molecular surfaces in AmberTools: GIST , 2016, J. Comput. Chem..

[43]  A. Ullrich,et al.  Somatic mutation in the ACK1 ubiquitin association domain enhances oncogenic signaling through EGFR regulation in renal cancer derived cells , 2010, Molecular oncology.

[44]  J. Åqvist,et al.  The linear interaction energy method for predicting ligand binding free energies. , 2001, Combinatorial chemistry & high throughput screening.

[45]  P. Mak,et al.  Crystal Structures of the Phosphorylated and Unphosphorylated Kinase Domains of the Cdc42-associated Tyrosine Kinase ACK1* , 2004, Journal of Biological Chemistry.

[46]  J. A. Barker,et al.  Monte Carlo studies of the dielectric properties of water-like models , 1973 .

[47]  George M Whitesides,et al.  Designing ligands to bind proteins , 2005, Quarterly Reviews of Biophysics.

[48]  N. Walker,et al.  Synthesis and optimization of substituted furo[2,3-d]-pyrimidin-4-amines and 7H-pyrrolo[2,3-d]pyrimidin-4-amines as ACK1 inhibitors. , 2012, Bioorganic & medicinal chemistry letters.

[49]  J. Åqvist,et al.  Ion-water interaction potentials derived from free energy perturbation simulations , 1990 .

[50]  Daniel Cappel,et al.  Accurate Binding Free Energy Predictions in Fragment Optimization , 2015, J. Chem. Inf. Model..

[51]  Julien Michel,et al.  Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations , 2017, bioRxiv.

[52]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[53]  Michael R. Shirts,et al.  Statistically optimal analysis of samples from multiple equilibrium states. , 2008, The Journal of chemical physics.

[54]  Jian Yin,et al.  Overview of the SAMPL5 host–guest challenge: Are we doing better? , 2016, Journal of Computer-Aided Molecular Design.

[55]  H. C. Andersen Molecular dynamics simulations at constant pressure and/or temperature , 1980 .

[56]  Julien Michel,et al.  Effects of Water Placement on Predictions of Binding Affinities for p38α MAP Kinase Inhibitors. , 2010, Journal of chemical theory and computation.

[57]  Xiaolin Hao,et al.  Identification and optimization of N3,N6-diaryl-1H-pyrazolo[3,4-d]pyrimidine-3,6-diamines as a novel class of ACK1 inhibitors. , 2008, Bioorganic & medicinal chemistry letters.

[58]  David L. Mobley,et al.  The SAMPL4 host–guest blind prediction challenge: an overview , 2014, Journal of Computer-Aided Molecular Design.

[59]  R. Zwanzig High‐Temperature Equation of State by a Perturbation Method. I. Nonpolar Gases , 1954 .

[60]  Jennifer L. Knight,et al.  Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. , 2015, Journal of the American Chemical Society.