RT Journal Article
SR Electronic
T1 Overview of the SAMPL6 host-guest binding affinity prediction challenge
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 371724
DO 10.1101/371724
A1 Andrea Rizzi
A1 Steven Murkli
A1 John N. McNeill
A1 Wei Yao
A1 Matthew Sullivan
A1 Michael K. Gilson
A1 Michael W. Chiu
A1 Lyle Isaacs
A1 Bruce C. Gibb
A1 David L. Mobley
A1 John D. Chodera
YR 2018
UL http://biorxiv.org/content/early/2018/09/25/371724.abstract
AB Accurately predicting the binding affinities of small organic molecules to biological macro-molecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macro-molecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host-guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host-guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from 10 participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host-guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host-guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host-guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states) may be required to further enhance predictive accuracy.List of abbreviationsAM1-BCCAustin model 1 bond charge correction [54, 55]AMOEBAatomic multipole optimized energetics for biomolecular simulation [96]B3LYPBecke 3-parameter Lee-Yang-Parr exchange-correlation functional [13]B3PW91Becke 3-parameter Perdew-Wang 91 exchange-correlation functional [13]CGenFFCHARMM generalized force field [120]COSMO-RSconductor-like screening model for real solvents [61]DDMdouble decoupling method [39]DFT-D3density functional theory with the D3 dispersion corrections [42]FMForce Matching [28]FSDAMFast switching double annihilation method [92, 97]GAFFgeneralized AMBER force field [121]HREXHamiltonian replica exchange [113]KECSAknowledge-based and empirical combined scoring algorithm [129]KMTISMKECSA-Movable Type Implicit Solvation Model [131]MDmolecular dynamicsMMPBSAmolecular mechanics Poisson Boltzmann/solvent accessible surface area [110]MovTypMovable Type method [130]OPLS3optimized potential for liquid simulations [45]PBSAPoisson-Boltzmann surface area [106]PM6-DH+PM6 semiempirical method with dispersion and hydrogen bonding corrections [64,100]RESPrestrained electrostatic potential [12]RESTreplica exchange with solute torsional tempering [68, 71]RFECrelative free energy calculationQM/MMmixed quantum mechanics and molecular mechanicsSOMDdouble annihilation or decoupling method performed with Sire/OpenMM6.3 software [27, Woods et al.]SQMsemi-empirical quantum mechanicsTIP3Ptransferable interaction potential three-point [57]TPSSTao, Perdew, Staroverov, and Scuseria exchange functional [116]USumbrella sampling [119]VSGB2.1VSGB2.0 solvation model reft to OPLS2.1/3/3e [67]