European Life Science Bootcamp: Case Studies in Enhanced Sampling Methods.

Transcription

1 European Life Science Bootcamp: Case Studies in Enhanced Sampling Methods

2 Outline Limitations of molecular dynamics Overcoming them with Enhanced sampling Pragmatic use of enhanced sampling methods P38 glycine flip conformational transition via metadynamics Permeation of a small molecule in membrane via metadynamics Conformational sampling through REST Get more from the backend

3 Historic Problems with Molecular-Dynamics in Drug-Discovery Historically three main issues have prevented the use of molecular-dynamics in industrial drug-discovery: Force field accuracy and reliability Protein and DNA force-fields were readily available Parameters for novel small-molecules had to be generated by hand for each system Speed Molecular-dynamics simulations were simply too CPU-intensive for the computer-hardware that was available Run times of weeks or months are simply not practical in an industrial setting Sampling Interesting events are slow on the molecular-dynamics time-scale Weeks to months of CPU-time, frequently generated nothing of interest

4 OPLS3: improved forcefield MD vs PDB (Asp Chi1) Virtual sites PDB OPLS2005 OPLS3 protein torsion reparametrized better coverage of small molecule torsions (95% of torsions, MMFF is 6%) Virtual sites for sigma-holes in hydrogen bonding and lone pairs QM OPLS2005 OPLS3

5 Desmond on GPU 350 DHFR system (23K atoms): full solvated system with empirical force field Performance (ns/day) K20 K40 K80 Due to HW and efforts from DE Shaw research in software performance improvements (also in memory footprint)

6 Chemical and Biological Timescales Methyl Rotations Larger Domain Motion / Conformational Changes Sidechain Rotamers Small Soluble FastFolding Proteins Water Equilibration Small Co-solvent Equilbration Ligand Diffusion Across Membranes Ligand Binding Molecular Dynamics on CPU Clusters or GPUs *Adapted from Henzler-Wildman and Kern, Nature, 2007, 450, years 10 years 100 (s) 10-3 (ms) 10-6 (μs) 10-9 (ns) (ps) (fs) Loop Motion / Coupled Loop Motion Bond Vibrations

7 The Advantages of Molecular-Dynamics Full solvent molecular dynamics overcomes limitations due to implicit solvation and conformational sampling Molecules are represented in an all atom format Protein and ligands are, by default, fully flexible The solvent, not necessarily just water, is explicitly modelled As close to the real system as is possible within the classical confines of a force-field When analysed carefully, it is possible to get atom-level detail for the events under simulation As the system evolves naturally, unexpected results can (and do) occur These can yield genuine insights into the underlying physical processes

8 The Disadvantages of Molecular-Dynamics High computational burden Even with multi-teraflop GPUs, molecular-dynamics simulations are still very computationally demanding Data intensive Lots of data is produced and this should be also carefully processed The development-level of the toolchain Packages such as Desmond, when coupled with the Maestro interface, lower the difficulty of setting up and using molecular-dynamics Backend infrastructure surely helps in getting the most out of your data General unfamiliarity with molecular-dynamics in drug discovery Modellers in pharma context have strong background in docking /pharmacophore and medchem but less in stat mech Even when expert users are present the impact of MD is historically considered marginal to solve real-world problems Is this really worth?

9 Overcoming limitations: enhanced sampling 100 (s) 10-3 (ms) 10-6 (μs) 10-9 (ns) (ps) (fs) Loop Motion / Coupled Loop Motion Bond Vibrations Methyl Rotations Larger Domain Motion / Conformational Changes Small Soluble FastFolding Proteins Water Equilibration Small Co-solvent Equilbration Ligand Diffusion Across Membranes Ligand Binding Molecular Dynamics on CPU Clusters or GPUs *Adapted from Henzler-Wildman and Kern, Nature, 2007, 450, years 10 years Effective boost from enhanced sampling Effective boost from enhanced sampling Sidechain Rotamers

10 Access points in Maestro (Applications View) Discussed in previous bootcamp Many enhanced sampling methods available in Maestro Simulated Annealing Replica Exchange Replica-exchange Solute Tempering (REST)-in the backend Metadynamics And analysis tools are also useful Method specific Metadynamics Analysis Replica Exchange Review Generic Simulation Event Analysis Simulation Quality Analysis

11 Examples of practical use of enhanced sampling techniques

12 P38 hinge glycine flip SD-006 (PDBid: 3HL7) PH (PDBid: 3HLL) Flipping is due to a glycine that belongs to a peculiar hinge motif TXXXG which is characteristic of p38 This allows the formation of a double hydrogen bond and provide superior selectivity Bioinformatics analysis* suggests the flip is energetically accessible Here we want to use metadynamics to evidence this possibility *Xing et al., Biochemistry 2009, 48

13 Metadynamics Add an history dependent potential (sum of Gaussians) along a degree of freedom (an approximation of the reaction coordinate) called collective variable (CV) The (negative) sum of the gaussian potentials are an estimate of the free energy Minimal overhead on Desmond GPU (for simple variable) Can be used in more than one dimension [1] Laio & Parrinello, PNAS 2002, vol. 20, p [2] Bussi & Laio, Parrinello Phys. Rev. Lett. 2006, vol 96, p

14 The workflow Structure cleaning Add missing loop PPW (protonation, flip sc, etc) System builder (solvent, counterions) Thermalization Metadynamics

15 Figuring out the CVs Grey ribbon: native Cyan ribbon: 3HLL 1 The torsions handling the look good but Other torsions move less? Should we add them as CVs? Other atoms seem to stand still but are practically moving What about solvent? This method requires low number of CVs. Bear in mind this limitaiton.

16 Metadynamics Panel 1 2 You can add many variables depending on the problem: 1 is optimal, 2 is doable, 3 is hard (to converge and interpret) The height and witdth of the gaussians are careful chosen defaults which should work for most of cases Many more variables accessible from the backend (cog wheel>write-> edit the msj file) How many ns? That s depending on your system, the CVs and the convergence you want to achieve. 3 4

17 Dihedral #1 (deg) Dihedral #2 (deg) Dihedral #2 (deg) Dihedral #1/ Dihedral #2(deg) Dihedral #1 (deg) Evolution of the free energy landscape Simulation time (ns) $JOBNAME.cvseq contains the plot of the CVS as they are produced The free energy estimate changes as you explore the landscape The more you explore two basins, the better will be their free energy difference estimate (via averaging)

18 The Metadynamics Analysis panel Produce the last free energy estimate from the sum of Gaussians (kerseq file) Interactively click and see the conformations associated to points in the collective variable space Non native state is accessible together with other many states within few kbt. Glycine introduce a large flexibility

19 About free energy: how much should I trust it? Metadynamics produces a brand new free energy estimate every time that the CV space is explored again This is called recrossing : more recrossings, more reliable is the result Do not worry of remarkable differences in the free energy: the cvs are not the true reaction coordinate but the message should be consistent With standard Metadynamics one should do averaging of independent free energy estimates Metadynamics in desmond is mostly an explorative tool. The free energy estimate requires more analysis Recrossing! Hooray! Got to the other state! Should I stop it? Wait! Wait! Dihedral #1/ Dihedral #2(deg) Now you can estimate the FES Occurrence of resampling of the initial basin Simulation time (ns) Now you have a new estimate of the fes. Averaging?

20 Metadynamics considerations and troubleshooting 1. Metadynamics is knowledge based 2. Metadynamics is not like docking: once the calculation is over you need to check if that s enough or if it is correct at all 3. Sometimes you do not really need the free energy, but you just want to know if a certain transition is possible within a reasonable energy: make sure you do multiple copy and obtain similar barriers 4. Sometimes you do not get to the state you want in the simulation time. No need to start from scratch but you can recycle the old potential (ask me if you are interested) 5. Sometimes you pile up lots of energy and nothing happens: have you chosen the right CV? Trajectory is your friend! 6. On the Metadynamics panel you have just three CVs but you can have access to many more CVs from the backend and virtually infinite from the desmond native enhanced sampling plugin

21 Case Study: EphB4 Inhibitors The two ligands on the right have approximately the same activity on EphB4* Ligand However, ligand 1 is 130x more potent in the cellular assay IC50(EphB4) Cellular IC50 40nM 172nM 23nM 22mM There are two possibilities: Ligand 2 has membrane permeability issues Ligand 2 is subject to efflux Metadynamics cannot comment directly on the efflux status of either molecule, but does allow us to investigate membrane permeability *Bardelle et al. J. Bioorg. Med. Chem. Lett. 2008, 18,

22 Confining potential zdist zdist Setup: membrane slab A slab configuration (here 4 units shown) it is an idealization: excessive concentration of ligands on the surface Artificial suppression of the membrane motions When Ligand permeation occurs, this happens simultaneously on all the cell close to it, therefore the perturbation is more significant than on a larger membrane on the membrane It is fast: 90 ns/day We can exploit the problem symmetry to optimize the sampling time: the ligand is only allowed to get to the center of the membrane and back in the solvent Membrane motion are slow. Long run are expected to converge the landscape Welltempered metadynamics is used here: put a maximum amount of hills and see if one of those make through the membrane Phosphate position in Z slightly restrained

23 Free Energy Zdist Ligand 2 Ligand 1 Simulation time (ns) Well-tempered Metadynamics puts a cap on the energy: if the barrier is lower than that cap, then the barrier is passed otherwise is not. These calculations benefit from doing in multiple copies for consensus Note: I intentionally did not plot the free energy since I know that might be very noisy WTmeta Membrane Free Energy Zdist (Ang) Solvent WTmeta Observations Zdist

24 Other uses of metadynamics With what you have available in the interface you can study for example Formation of hbonds or specific interactions (distance CV) Permeation of ions in channels (zdist coupled with specific sidechain interactions) Docking pose stability (via distances, angles and dihedrals) You can extend it further by using the Enhanced Sampling Plugin from Desmond (see Desmond users guide, chapter 11) Center of masses RMSD Atomic coordination Cartesian positions Any function of all the above mentioned

25 Small-Molecule Conformer Generation Small-molecule conformer-generators (Confgen, Macromodel) work well when it comes to generating a diverse range of conformers However few, if any, of these programs are designed to accurately model the behaviour of a small-molecule in solution Frequently the energy model used is very crude Even quite advanced GB-models, such as the one in MacroModel, have limitations when it comes to accurately balancing intramolecular-interactions with ligand-solvent interactions Occasionally we need to get a more detailed and accurate assessment of a small-molecule s behaviour in solution Molecular-dynamics, with an explicit solvent, provides a significantly more detailed method of exploring the ensemble of structures adopted by a small-molecule in solution which includes microsolvation effects

26 Replica-Exchange An Alternative Approach to the Sampling Problem Simulation N T = Very Hot Instead of running a single simulation at 300K, we run a set of parallel simulations The additional simulations are run at higher temperatures This allows those simulations to escape potential minima that would trap the system at 300K Simulation... T = Hot At various intervals an attempt is made to exchange structures between the simulations Simulation... T = Quite Hot Simulation... T = Warmer still Simulation 2 T = Warmer Simulation 1 T = 300K Time If a high-temperature structure has a plausible existence at a lower temperature it is swapped This allows information from the high-temperature simulations to percolate down to the lower levels Two replica-exchange approaches are implemented in the Schrödinger suite REMD Replica-Exchange Molecular-Dynamics: all the system is heathen up REST Replica-Exchange with Solute-Tempering: only part of the system is heathen up. Improved efficiency (although less general) $SCHRODINGER/run -FROM desmond create_rest_md.py -asl "res.num 1" -ref_temperature ensemble NPT -forcefield OPLS3 -time n_replica $NP Hot region! -trajectory_interval np $NP -do_not_run sys.mae -host ${MYHOST} (-use_gpu)

27 Case Study 1EVE and 2ITZ Correct understanding of intramolecular hydrogen bond can be relevant in live projects [1] 1EVE and 2ITZ are fairly typical drug-like molecules 1EVE is relatively rigid, with few opportunities for intra-molecular interactions 2ITZ contains considerably more flexibility and has the ability to form a strong, charged, intramolecular hydrogen-bond These provide a good opportunity to explore the differences in strain-energy calculated by MacroModel and arising from a molecular-dynamics simulation* * OPLS-3, 50ns, 12-replica REST simulation in TIP4P M NaCl [1]Kuhn, Mohr, Stahl, JMC, 2010, 53

28 Replica Exchange Dynamics Review panel 50 ns MD, 12 replicas in solvent If this graph is almost flat: good. Check no holes appear in this histogram! Check for different replicas Half of the temperature swaps are accepted (even too good! 0.25 is also ok). The temperature is effectively exploited by the replicas See how each replica travels in the temperature space

29 Obtaining Energies from a Molecular-Dynamics Simulation MacroModel can calculate an energy for any structure it is passed However, in the presence of explicit-solvent, calculating a meaningful energy is not so easy Two identical ligand conformations may have a very different solventstructure surrounding them We want the energy of the average solvent structure *MacroModel will run this calculation happily **$SCHRODINGER/run conformer_cluster.py t_rms all_et... We can get a handle on this average energy via clustering Minimise the snapshots from the MD-simulation This moves every snapshot to the nearest local-minima and calculates an energy value which we can use for comparison later* Cluster the minimised snapshots Torsion based clustering works well** Use Boltzmann s equation to relate the cluster sizes to the relative energy of each cluster

30 MD/Boltzmann Relative Energy (kcal/mol) 1EVE MacroModel vs. Molecular-Dynamics MacroModel Relative Energy (kcal/mol) EVE shows good agreement between the MacroModel (implicit-solvent) energies and those derived from the molecular-dynamics simulation Four conformers (clearly related) make up the majority (60%) of the solution-phase conformers

31 MD/Boltzmann Relative Energy (kcal/mol) 2ITZ MacroModel vs. Molecular-Dynamics In the case of 2ITZ we see very poor agreement between the MacroModel and molecular-dynamics energies Essentially there are two clusters in the results The first cluster has an internal hydrogen-bond between the morpholine and the di-oxyquinazoline ring system MacroModel Relative Energy (kcal/mol) This is heavily rewarded by MacroModel which clearly has no idea of the entropic penalty for maintaining this conformation nor any real idea of the morpholinesolvent interactions The second cluster has the morpholine floating freely In the molecular-dynamics simulation, this is completely acceptable as the solvation of the amine compensates for any loss of internal bonding MacroModel suggests that these conformations have a 2.5kcal/mol penalty

32 2ITZ EGFR/Iressa The bound form of 2ITZ (Iressa) shows the ligand with the noninternally hydrogen-bonded conformation With a Ki(EGFR)=2.1nM, it is highly unlikely that this conformation has the 2.5kcal/mol penalty indicated by MacroModel

33 Acknowledgments Dan Robinson (most of this material comes from him actually) Dima Lupyan Jas Bhachoo Thank you!