Abstract
Use of quantum mechanical/molecular mechanical (QM/MM) methods in binding free energy calculations, particularly in the SAMPL challenge, often fail to achieve improvement over standard additive (MM) force fields. Frequently, the implementation is through use of reference potentials, or the so-called “indirect approach”, and inherently relies on sufficient overlap existing between MM and QM/MM configurational spaces. This overlap is generally poor, particularly for the use of free energy perturbation to perform the MM to QM/MM free energy correction at the end states of interest (e.g., bound and unbound states). However, by utilizing MM parameters that best reproduce forces obtained at the desired QM level of theory, it is possible to lessen the configurational disparity between MM and QM/MM. To this end, we sought to use force matching to generate MM parameters for the SAMPL6 CB[8] host–guest binding challenge, classically compute binding free energies, and apply energetic end state corrections to obtain QM/MM binding free energy differences. For the standard set of 11 molecules and the bonus set (including three additional challenge molecules), error statistics, such as the root mean square deviation (RMSE) were moderately poor (5.5 and 5.4 kcal/mol). Correlation statistics, however, were in the top two for both standard and bonus set submissions (\(R^{2}\) of 0.42 and 0.26, \(\tau\) of 0.64 and 0.47 respectively). High RMSE and moderate correlation strongly indicated the presence of systematic error. Identifiable issues were ameliorated for two of the guest molecules, resulting in a reduction of error and pointing to strong prospects for the future use of this methodology.
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Notes
Herein, MM[FM] [which denotes MM with bonded parameters obtained from force matching, charges from RESP(SMD), and LJ from CGenFF] will be referred to simply as FM, and QM will be a placeholder for either B3LYP/LANL2DZ (for guest G13) or B3LYP/6-31G(d) for all other guest/host.
References
Guthrie JP (2009) A blind challenge for computational solvation free energies: introduction and overview. J Phys Chem B 113(14):4501–4507. https://doi.org/10.1021/jp806724u
Geballe MT, Skillman AG, Nicholls A, Guthrie JP, Taylor PJ (2010) The SAMPL2 blind prediction challenge: introduction and overview. J Comput Aided Mol Des 24(4):259–279. https://doi.org/10.1007/s10822-010-9350-8
Muddana HS, Daniel Varnado C, Bielawski CW, Urbach AR, Isaacs L, Geballe MT, Gilson MK (2012) Blind prediction of host-guest binding affinities: a new SAMPL3 challenge. J Comput Aided Mol Des 26(5):475–487. https://doi.org/10.1007/s10822-012-9554-1
Muddana HS, Fenley AT, Mobley DL, Gilson MK (2014) The SAMPL4 host-guest blind prediction challenge: an overview. J Comput Aided Mol Des 28(4):305–317. https://doi.org/10.1007/s10822-014-9735-1
Yin J, Henriksen NM, Slochower DR, Shirts MR, Chiu MW, Mobley DL, Gilson MK (2016) Overview of the SAMPL5 host-guest challenge: are we doing better? J Comput Aided Mol Des 31(1):1–19. https://doi.org/10.1007/s10822-016-9974-4
Zacharias M, Straatsma TP, McCammon JA (1994) Separation-shifted scaling, a new scaling method for Lennard-Jones interactions in thermodynamic integration. J Chem Phys 100(12):9025–9031. https://doi.org/10.1063/1.466707
Beutler TC, Mark AE, van Schaik RC, Gerber PR, van Gunsteren WF (1994) Avoiding singularities and numerical instabilities in free energy calculations based on molecular simulations. Chem Phys Lett 222(6):529–539. https://doi.org/10.1016/0009-2614(94)00397-1
Chipot C, Pohorille A (2007) Free energy calculations. Springer, Berlin. https://doi.org/10.1007/978-3-540-38448-9
Ryde U, Söderhjelm P (2016) Ligand-binding affinity estimates supported by quantum-mechanical methods. Chem Rev 116(9):5520–5566. https://doi.org/10.1021/acs.chemrev.5b00630
Mackerell AD, Feig M, Brooks CL (2004) Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J Comput Chem 25(11):1400–1415. https://doi.org/10.1002/jcc.20065
Manzoni F, Söderhjelm P (2014) Prediction of hydration free energies for the SAMPL4 data set with the AMOEBA polarizable force field. J Comput Aided Mol Des 28(3):235–244. https://doi.org/10.1007/s10822-014-9733-3
Gao J, Xia X (1992) A priori evaluation of aqueous polarization effects through Monte Carlo QM-MM simulations. Science 258(5082):631–635. https://doi.org/10.1126/science.1411573
Gao J, Luque FJ, Orozco M (1993) Induced dipole moment and atomic charges based on average electrostatic potentials in aqueous solution. J Chem Phys 98(4):2975–2982. https://doi.org/10.1063/1.464126
Luzhkov V, Warshel A (1992) Microscopic models for quantum mechanical calculations of chemical processes in solutions: Ld/ampac and SCAAS/AMPAC calculations of solvation energies. J Comput Chem 13(2):199–213. https://doi.org/10.1002/jcc.540130212
Wesolowski T, Warshel A (1994) Ab initio free energy perturbation calculations of solvation free energy using the frozen density functional approach. J Phys Chem 98(20):5183–5187. https://doi.org/10.1021/j100071a003
Gao J, Freindorf M (1997) Hybrid ab initio QM/MM simulation ofN-methylacetamide in aqueous solution. J Phys Chem A 101(17):3182–3188. https://doi.org/10.1021/jp970041q
Zheng YJ, Merz KM (1992) Mechanism of the human carbonic anhydrase II-catalyzed hydration of carbon dioxide. J Am Chem Soc 114(26):10498–10507. https://doi.org/10.1021/ja00052a054
Zwanzig RW (1954) High-temperature equation of state by a perturbation method. i. nonpolar gases. J Chem Phys 22(8):1420–1426. https://doi.org/10.1063/1.1740409
König G, Hudson PS, Boresch S, Woodcock HL (2014) 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. J Chem Theory Comput 10(4):1406–1419. https://doi.org/10.1021/ct401118k
Hudson PS, White JK, Kearns FL, Hodoscek M, Boresch S, Lee Woodcock H (2015a) Efficiently computing pathway free energies: new approaches based on chain-of-replica and non-Boltzmann Bennett reweighting schemes. Biochim Biophys Acta 1850(5):944–953. https://doi.org/10.1016/j.bbagen.2014.09.016
Hudson PS, Woodcock HL, Boresch S (2015b) Use of nonequilibrium work methods to compute free energy differences between molecular mechanical and quantum mechanical representations of molecular systems. J Phys Chem Lett 6(23):4850–4856. https://doi.org/10.1021/acs.jpclett.5b02164
Boresch S, Woodcock HL (2016) Convergence of single-step free energy perturbation. Mol Phys 115(9–12):1200–1213. https://doi.org/10.1080/00268976.2016.1269960
Kearns FL, Hudson PS, Woodcock HL, Boresch S (2017) Computing converged free energy differences between levels of theory via nonequilibrium work methods: challenges and opportunities. J Comput Chem 38(16):1376–1388. https://doi.org/10.1002/jcc.24706
Hudson PS, Boresch S, Rogers DM, Woodcock HL (2018) Accelerating QM/MM free energy computations via intramolecular force matching. J Chem Theory Comput (in revision)
Jarzynski C (1997) Nonequilibrium equality for free energy differences. Phys Rev Lett 78(14):2690–2693. https://doi.org/10.1103/physrevlett.78.2690
Csányi G, Albaret T, Payne MC, De Vita A (2004) “Learn on the fly”: a hybrid classical and quantum-mechanical molecular dynamics simulation. Phys Rev Lett. https://doi.org/10.1103/physrevlett.93.175503
Akin-Ojo O, Song Y, Wang F (2008) Developing ab initio quality force fields from condensed phase quantum-mechanics/molecular-mechanics calculations through the adaptive force matching method. J Chem Phys 129(6):064108. https://doi.org/10.1063/1.2965882
Akin-Ojo O, Wang F (2010) The quest for the best nonpolarizable water model from the adaptive force matching method. J Comput Chem 32(3):453–462. https://doi.org/10.1002/jcc.21634
Wang F, Akin-Ojo O, Pinnick E, Song Y (2011) Approaching post-hartree-fock quality potential energy surfaces with simple pair-wise expressions: parameterising point-charge-based force fields for liquid water using the adaptive force matching method. Mol Simul 37(7):591–605. https://doi.org/10.1080/08927022.2011.565759
Pinnick ER, Calderon CE, Rusnak AJ, Wang F (2012) Achieving fast convergence of ab initio free energy perturbation calculations with the adaptive force-matching method. Theor Chem Acc. https://doi.org/10.1007/s00214-012-1146-6
Li J, Wang F (2015) Pairwise-additive force fields for selected aqueous monovalent ions from adaptive force matching. J Chem Phys 143(19):194505. https://doi.org/10.1063/1.4935599
Wang LP, Van Voorhis T (2010) Communication: hybrid ensembles for improved force matching. J Chem Phys 133(23):231101. https://doi.org/10.1063/1.3519043
Wang LP, Chen J, Van Voorhis T (2012) Systematic parametrization of polarizable force fields from quantum chemistry data. J Chem Theory Comput 9(1):452–460. https://doi.org/10.1021/ct300826t
Wang LP, McKiernan KA, Gomes J, Beauchamp KA, Head-Gordon T, Rice JE, Swope WC, Martínez TJ, Pande VS (2017) Building a more predictive protein force field: a systematic and reproducible route to AMBER-FB15. J Phys Chem B 121(16):4023–4039. https://doi.org/10.1021/acs.jpcb.7b02320
Rogers DM (2016) ForceSolve. https://github.com/frobnitzem/forcesolve. Accessed 20 July 2017
Rogers DM (2018) ChemParam. https://github.com/frobnitzem/chemparam. Accessed 20 July 2017
Brooks BR, Brooks CL, Mackerell AD, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S, Caflisch A, Caves L, Cui Q, Dinner AR, Feig M, Fischer S, Gao J, Hodoscek M, Im W, Kuczera K, Lazaridis T, Ma J, Ovchinnikov V, Paci E, Pastor RW, Post CB, Pu JZ, Schaefer M, Tidor B, Venable RM, Woodcock HL, Wu X, Yang W, York DM, Karplus M (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30(10):1545–1614. https://doi.org/10.1002/jcc.21287
Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell AD (2009) CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem. https://doi.org/10.1002/jcc.21367
Gilson M, Given J, Bush B, McCammon J (1997) The statistical-thermodynamic basis for computation of binding affinities: a critical review. Biophys J 72(3):1047–1069. https://doi.org/10.1016/s0006-3495(97)78756-3
Boresch S, Tettinger F, Leitgeb M, Karplus M (2003) Absolute binding free energies: a quantitative approach for their calculation. J Phys Chem B 107(35):9535–9551. https://doi.org/10.1021/jp0217839
Han K, Hudson PS, Jones MR, Nishikawa N, Tofoleanu F, Brooks BR (2018) Prediction of cb[8] host-guest binding free energies in sampl6 using the double-decoupling method. J Comput Aided Mol Des. https://doi.org/10.1007/s10822-018-0144-8
Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Petersson GA, Nakatsuji H, Li X, Caricato M, Marenich AV, Bloino J, Janesko BG, Gomperts R, Mennucci B, Hratchian HP, Ortiz JV, Izmaylov AF, Sonnenberg JL, Williams-Young D, Ding F, Lipparini F, Egidi F, Goings J, Peng B, Petrone A, Henderson T, Ranasinghe D, Zakrzewski VG, Gao J, Rega N, Zheng G, Liang W, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Throssell K, Montgomery JA Jr, Peralta JE, Ogliaro F, Bearpark MJ, Heyd JJ, Brothers EN, Kudin KN, Staroverov VN, Keith TA, Kobayashi R, Normand J, Raghavachari K, Rendell AP, Burant JC, Iyengar SS, Tomasi J, Cossi M, Millam JM, Klene M, Adamo C, Cammi R, Ochterski JW, Martin RL, Morokuma K K, Farkas O, Foresman JB, Fox DJ (2016) Gaussian 16 revision B.01. Gaussian Inc., Wallingford
Becke AD (1993) Density-functional thermochemistry. III. the role of exact exchange. J Chem Phys 98(7):5648–5652. https://doi.org/10.1063/1.464913
Lee C, Yang W, Parr RG (1988) Development of the colle-salvetti correlation-energy formula into a functional of the electron density. Phys Rev B 37(2):785–789. https://doi.org/10.1103/physrevb.37.785
Møller C, Plesset MS (1934) Note on an approximation treatment for many-electron systems. Phys Rev 46(7):618–622. https://doi.org/10.1103/physrev.46.618
Hariharan PC, Pople JA (1973) The influence of polarization functions on molecular orbital hydrogenation energies. Theor Chim Acta 28(3):213–222. https://doi.org/10.1007/bf00533485
Francl MM, Pietro WJ, Hehre WJ, Binkley JS, Gordon MS, DeFrees DJ, Pople JA (1982) Self-consistent molecular orbital methods. XXIII. a polarization-type basis set for second-row elements. J Chem Phys 77(7):3654–3665. https://doi.org/10.1063/1.444267
Marenich AV, Cramer CJ, Truhlar DG (2009) Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. J Phys Chem B 113(18):6378–6396. https://doi.org/10.1021/jp810292n
Bayly CI, Cieplak P, Cornell W, Kollman PA (1993) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model. J Phys Chem 97(40):10269–10280. https://doi.org/10.1021/j100142a004
Wang J, Wang W, Kollman PA, Case DA (2006) Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Model 25(2):247–260. https://doi.org/10.1016/j.jmgm.2005.12.005
Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comp Chem 25(9):1157–1174. https://doi.org/10.1002/jcc.20035
Vanommeslaeghe K, Raman EP, MacKerell AD (2012) Automation of the CHARMM general force field (CGenFF) II: assignment of bonded parameters and partial atomic charges. J Chem Inf Model 52(12):3155–3168. https://doi.org/10.1021/ci3003649
Yao S, Plastaras JP, Marzilli LG (1994) A molecular mechanics AMBER-type force field for modeling platinum complexes of guanine derivatives. Inorg Chem 33(26):6061–6077. https://doi.org/10.1021/ic00104a015
Ojeda-May P, Nam K (2017) Acceleration of semiempirical QM/MM methods through message passage interface (MPI), hybrid MPI/Open multiprocessing, and self-consistent field accelerator implementations. J Chem Theory Comput 13(8):3525–3536. https://doi.org/10.1021/acs.jctc.7b00322
Thiel W, Voityuk AA (1996) Extension of MNDO to d orbitals: parameters and results for the second-row elements and for the zinc group. J Phys Chem 100(2):616–626. https://doi.org/10.1021/jp952148o
Hay PJ, Wadt WR (1985) Ab initio effective core potentials for molecular calculations. Potentials for K to Au including the outermost core orbitals. J Chem Phys 82(1):299–310. https://doi.org/10.1063/1.448975
Wadt WR, Hay PJ (1985) Ab initio effective core potentials for molecular calculations. Potentials for main group elements Na to Bi. J Chem Phys 82(1):284–298. https://doi.org/10.1063/1.448800
Hay PJ, Wadt WR (1985) Ab initio effective core potentials for molecular calculations. Potentials for the transition metal atoms Sc to Hg. J Chem Phys 82(1):270–283. https://doi.org/10.1063/1.448799
Shao Y, Gan Z, Epifanovsky E, Gilbert AT, Wormit M, Kussmann J, Lange AW, Behn A, Deng J, Feng X, Ghosh D, Goldey M, Horn PR, Jacobson LD, Kaliman I, Khaliullin RZ, Kuś T, Landau A, Liu J, Proynov EI, Rhee YM, Richard RM, Rohrdanz MA, Steele RP, SundstromEJ Woodcock HL, Zimmerman PM, Zuev D, Albrecht B, Alguire E, AustinB Beran GJO, Bernard YA, Berquist E, Brandhorst K, Bravaya KB, Brown ST, Casanova D, Chang CM, Chen Y, Chien SH, Closser KD, Crittenden DL, Diedenhofen M, DiStasio RA, Do H, Dutoi AD, Edgar RG, Fatehi S, Fusti-Molnar L, Ghysels A, Golubeva-Zadorozhnaya A, GomesJ Hanson-Heine MW, Harbach PH, Hauser AW, Hohenstein EG, Holden ZC, Jagau TC, Ji H, Kaduk B, Khistyaev K, Kim J, Kim J, King RA, Klunzinger P, Kosenkov D, Kowalczyk T, Krauter CM, Lao KU, LaurentAD Lawler KV, Levchenko SV, Lin CY, Liu F, Livshits E, Lochan RC, Luenser A, Manohar P, Manzer SF, Mao SP, Mardirossian N, MarenichAV Maurer SA, Mayhall NJ, Neuscamman E, Oana CM, Olivares-Amaya R, O’Neill DP, Parkhill JA, Perrine TM, Peverati R, Prociuk A, RehnDR Rosta E, Russ NJ, Sharada SM, Sharma S, Small DW, Sodt A, SteinT Stück D, Su YC, Thom AJ, Tsuchimochi T, Vanovschi V, VogtL Vydrov O, Wang T, Watson MA, Wenzel J, White A, Williams CF, YangJ Yeganeh S, Yost SR, You ZQ, Zhang IY, Zhang X, Zhao Y, Brooks BR, Chan GK, Chipman DM, Cramer CJ, Goddard WA, Gordon MS, Hehre WJ, Klamt A, Schaefer HF, Schmidt MW, Sherrill CD, Truhlar DG, WarshelA XuX, Aspuru-Guzik A, Baer R, Bell AT, Besley NA, Chai JD, DreuwA Dunietz BD, Furlani TR, Gwaltney SR, Hsu CP, Jung Y, Kong J, Lambrecht DS, Liang W, Ochsenfeld C, Rassolov VA, Slipchenko LV, Subotnik JE, Van Voorhis T, Herbert JM, Krylov AI, Gill PM, Head-Gordon M (2014) Advances in molecular quantum chemistrycontained in the q-chem 4 program package. Mol Phys 113(2):184–215. https://doi.org/10.1080/00268976.2014.952696
Rogers DM, Beck TL (2010) Resolution and scale independent function matching using a string energy penalized spline prior. ArXiv e-print http://arxiv.org/abs/1003.4741,
Rogers DM, Beck TL (2008) ForceSolve. http://forcesolve.sourceforge.net. Accessed 20 July 2017
Sugita Y, Kitao A, Okamoto Y (2000) Multidimensional replica-exchange method for free-energy calculations. J Chem Phys 113(15):6042–6051. https://doi.org/10.1063/1.1308516
Bennett CH (1976) Efficient estimation of free energy differences from Monte Carlo data. J Comput Phys 22(2):245–268. https://doi.org/10.1016/0021-9991(76)90078-4
Ryde U (2017) How many conformations need to be sampled to obtain converged QM/MM energies? The curse of exponential averaging. J Chem Theory Comput 13(11):5745–5752. https://doi.org/10.1021/acs.jctc.7b00826
Olsson MA, Söderhjelm P, Ryde U (2016) Converging ligand-binding free energies obtained with free-energy perturbations at the quantum mechanical level: full paper. J Comp Chem 37(17):1589–1600. https://doi.org/10.1002/jcc.24375
Olsson MA, Ryde U (2017) Comparison of QM/MM methods to obtain ligand-binding free energies. J Chem Theory Comput 13(5):2245–2253. https://doi.org/10.1021/acs.jctc.6b01217
Heimdal J, Ryde U (2012) Convergence of QM/MM free-energy perturbations based on molecular-mechanics or semiempirical simulations. Phys Chem Chem Phys 14(36):12592. https://doi.org/10.1039/c2cp41005b
Genheden S, Cabedo Martinez AI, Criddle MP, Essex JW (2014) Extensive all-atom Monte Carlo sampling and QM/MM corrections in the SAMPL4 hydration free energy challenge. J Comput Aid Mol Des 28(3):187–200. https://doi.org/10.1007/s10822-014-9717-3
Cave-Ayland C, Skylaris CK, Essex JW (2015) Direct validation of the single step classical to quantum free energy perturbation. J Phys Chem B 119(3):1017–1025. https://doi.org/10.1021/jp506459v
Sampson C, Fox T, Tautermann CS, Woods C, Skylaris CK (2015) A “stepping stone” approach for obtaining quantum free energies of hydration. J Phys Chem B 119(23):7030–7040. https://doi.org/10.1021/acs.jpcb.5b01625
Genheden S, Ryde U, Söderhjelm P (2015) Binding affinities by alchemical perturbation using QM/MM with a large QM system and polarizable MM model. J Comp Chem 36(28):2114–2124. https://doi.org/10.1002/jcc.24048
Wu D, Kofke DA (2004) Model for small-sample bias of free-energy calculations applied to gaussian-distributed nonequilibrium work measurements. J Chem Phys 121(18):8742–8747. https://doi.org/10.1063/1.1806413
Wu D, Kofke DA (2005) Phase-space overlap measures. i. fail-safe bias detection in free energies calculated by molecular simulation. J Chem Phys 123(5):054103. https://doi.org/10.1063/1.1992483
Hynninen AP, Crowley MF (2013) New faster CHARMM molecular dynamics engine. J Comput Chem 35(5):406–413. https://doi.org/10.1002/jcc.23501
Hoover WG (1985) Canonical dynamics: equilibrium phase-space distributions. Phys Rev A 31(3):1695–1697. https://doi.org/10.1103/physreva.31.1695
Shirts MR, Chodera JD (2008) Statistically optimal analysis of samples from multiple equilibrium states. J Chem Phys 129(12):124105. https://doi.org/10.1063/1.2978177
Boresch S, Karplus M (1999) The role of bonded terms in free energy simulations: 1. Theoretical analysis. J Phys Chem A 103(1):103–118. https://doi.org/10.1021/jp981628n
Giese TJ, York DM (2018) A GPU-accelerated parameter interpolation thermodynamic integration free energy method. J Chem Theory Comput 14(3):1564–1582. https://doi.org/10.1021/acs.jctc.7b01175
Stewart JJP (2007) Optimization of parameters for semiempirical methods v: modification of NDDO approximations and application to 70 elements. J Mol Model 13(12):1173–1213. https://doi.org/10.1007/s00894-007-0233-4
Cleveland T, Landis CR (1996) Valence bond concepts applied to the molecular mechanics description of molecular shapes. 2. Applications to hypervalent molecules of the p-block. J Am Chem Soc 118(25):6020–6030. https://doi.org/10.1021/ja9506521
Berrêdo RCd, Jorge FE (2010) All-electron double zeta basis sets for platinum: estimating scalar relativistic effects on platinum(II) anticancer drugs. J Mol Struct 961(1):107–112. https://doi.org/10.1016/j.theochem.2010.09.007 http://www.sciencedirect.com/science/article/pii/S0166128010005786
Wang K, Chodera JD, Yang Y, Shirts MR (2013) Identifying ligand binding sites and poses using GPU-accelerated Hamiltonian replica exchange molecular dynamics. J Comput Aided Mol Des 27(12):989–1007. https://doi.org/10.1007/s10822-013-9689-8
Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, Beauchamp KA, Wang LP, Simmonett AC, Harrigan MP, Stern CD, Wiewiora RP, Brooks BR, Pande VS (2017) OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol 13(7):e1005659. https://doi.org/10.1371/journal.pcbi.1005659
Rizzi A, Chodera J, Naden L, Beauchamp K, Grinaway P, Rustenburg B, Albanese S, Saladi S (2018) Choderalab/Yank: more post-sams bugfixes. https://doi.org/10.5281/zenodo.1226361
Chodera J, Rizzi A, Naden L, Beauchamp K, Grinaway P, Fass J, Rustenburg B, Ross GA, Simmonett A, Swenson DW (2018) Choderalab/openmmtools: 0.15.0 - restraint forces. https://doi.org/10.5281/zenodo.1205753
Rizzi A, Murkli S, McNeill JN, Yao W, Sullivan M, Gilson MK, Chiu MW, Isaacs L, Gibb BC, Mobley DL, Chodera JD (2018) Overview of the SAMPL6 host-guest binding affinity prediction challenge. J Comput Aid Mol Des. https://doi.org/10.1101/371724
Murkli S, McNeill JN, Isaacs L (2018) Cucurbit[8]uril-guest complexes: blinded dataset for the sampl6 challenge. Supramol Chem (submitted)
Acknowledgements
The authors would like to thank Rubén Meana-Pañeda, Richard Venable, John Legato, Qiao Zheng, and Michael R. Jones for technical assistance. We extend our gratitude to Erin Cassidy Hendrick, Ian Bookhamer, Stefan Boresch, Florentina Tofoleanu, and Andrea Rizzi for helpful comments on the manuscript and general insights. This work was partially supported by the intramural research program of the National Heart, Lung and Blood Institute (NHLBI) of the National Institutes of Health and employed the high-performance computational capabilities of the LoBoS and Biowulf Linux clusters at the National Institutes of Health. (http://www.lobos.nih.gov and http://biowulf.nih.gov). PSH acknowledges funding support from the Intramural Research Program of the NIH, NHLBI. HLW would like to highlight that this material is based upon work supported by the National Science Foundation under CHE-1464946.
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Hudson, P.S., Han, K., Woodcock, H.L. et al. Force matching as a stepping stone to QM/MM CB[8] host/guest binding free energies: a SAMPL6 cautionary tale. J Comput Aided Mol Des 32, 983–999 (2018). https://doi.org/10.1007/s10822-018-0165-3
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DOI: https://doi.org/10.1007/s10822-018-0165-3