SAMPL6 host–guest blind predictions using a non equilibrium alchemical approach

Methodological and force field effects in the molecular dynamics-based prediction of binding free energies of host-guest systems

As a contribution to the understanding and rationalization of methodological and modeling effects in recent host-guest SAMPL challenges, using an alchemical molecular dynamics technique we have examined the impact of force field parameterization and ionic strength in connection with guest charge neutralization on computed dissociation free energies in two typical SAMPL heavily charged macrocyclic hosts encapsulating small protonated amines with disparate binding affinities. We have shown that the methodological treatment for host neutralization, with explicit ions or with the background neutralizing plasma in the context of alchemical calculations under periodic boundary conditions, has a moderate effect on the calculated affinities. On the other hand, we have shown that seemingly small differences in the force field parameterization in highly symmetric hosts can produce systematic effects on the structural features that can have a significant impact on the predicted binding affinities.

Host Dynamics under General-Purpose Force Fields

Macrocyclic hosts as prototypical receptors to gaseous and drug-like guests are crucial components in pharmaceutical research. The external guests are often coordinated at the center of these macromolecular containers. The formation of host-guest coordination is accompanied by the broken of host-water and host-ion interactions and sometimes also involves some conformational rearrangements of the host. A balanced description of various components of interacting terms is indispensable. However, up to now, the modeling community still lacks a general yet detailed understanding of commonly employed general-purpose force fields and the host dynamics produced by these popular selections. To fill this critical gap, in this paper, we profile the energetics and dynamics of four types of popular macrocycles, including cucurbiturils, pillararenes, cyclodextrins, and octa acids. The presented investigations of force field definitions, refitting, and evaluations are unprecedently detailed. Based on the valuable observations and insightful explanations, we finally summarize some general guidelines on force field parametrization and selection in host-guest modeling.

Relative Binding Free Energy between Chemically Distant Compounds Using a Bidirectional Nonequilibrium Approach

In the context of advanced hit-to-lead drug design based on atomistic molecular dynamics simulations, we propose a dual topology alchemical approach for calculating the relative binding free energy (RBFE) between two chemically distant compounds. The method (termed NE-RBFE) relies on the enhanced sampling of the end-states in bulk and in the bound state via Hamiltonian Replica Exchange, alchemically connected by a series of independent and fast nonequilibrium (NE) simulations. The technique has been implemented in a bidirectional fashion, applying the Crooks theorem to the NE work distributions for RBFE predictions. The dissipation of the NE process, negatively affecting accuracy, has been minimized by introducing a smooth regularization based on shifted electrostatic and Lennard-Jones non bonded potentials. As a challenging testbed, we have applied our method to the calculation of the RBFEs in the recent host-guest SAMPL international contest, featuring a macrocyclic host with guests varying in the net charge, volume, and chemical fingerprints. Closure validation has been successfully verified in cycles involving compounds with disparate Tanimoto coefficients, volume, and net charge. NE-RBFE is specifically tailored for massively parallel facilities and can be used with little or no code modification on most of the popular software packages supporting nonequilibrium alchemical simulations, such as Gromacs, Amber, NAMD, or OpenMM. The proposed methodology bypasses most of the entanglements and limitations of the standard single topology RBFE approach for strictly congeneric series based on free-energy perturbation, such as slowly relaxing cavity water, sampling issues along the alchemical stratification, and the need for highly overlapping molecular fingerprints.

SAMPL9 blind predictions using nonequilibrium alchemical approaches

We present our blind predictions for the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL), ninth challenge, focusing on the binding of WP6 (carboxy-pillar[6]arene) with ammonium/diammonium cationic guests. Host-guest binding free energies have been calculated using the recently developed virtual double system single box approach, based on the enhanced sampling of the bound and unbound end-states followed by fast switching nonequilibrium alchemical simulations [M. Macchiagodena et al., J. Chem. Theory Comput. 16, 7160 (2020)]. As far as Pearson and Kendall coefficients are concerned, performances were acceptable and, in general, better than those we submitted for calixarenes, cucurbituril-like open cavitand, and beta-cyclodextrines in previous SAMPL host-guest challenges, confirming the reliability of nonequilibrium approaches for absolute binding free energy calculations. In comparison with previous submissions, we found a rather large mean signed error that we attribute to the way the finite charge correction was addressed through the assumption of a neutralizing background plasma.

Virtual Double-System Single-Box for Absolute Dissociation Free Energy Calculations in GROMACS

We describe a step-by-step protocol for the computation of absolute dissociation free energy with GROMACS code and PLUMED library, which exploits a combination of advanced sampling techniques and nonequilibrium alchemical methodologies. The computational protocol has been automated through an open source Python middleware (HPC_Drug) which allows one to set up the GROMACS/PLUMED input files for execution on high performing computing facilities. The proposed protocol, by exploiting its inherent parallelism and the power of the GROMACS code on graphical processing units, has the potential to afford accurate and precise estimates of the dissociation constants in drug-receptor systems described at the atomistic level. The procedure has been applied to the calculation of the absolute dissociation free energy of PF-07321332, an oral antiviral proposed by Pfizer, with the main protease (3CL^pro) of SARS-CoV-2.

Conformational Fluctuations in GTP-Bound K-Ras: A Metadynamics Perspective with Harmonic Linear Discriminant Analysis

Biomacromolecules often undergo significant conformational rearrangements during function. In proteins, these motions typically consist in nontrivial, concerted rearrangement of multiple flexible regions. Mechanistic, thermodynamics, and kinetic predictions can be obtained via molecular dynamics simulations, provided that the simulation time is at least comparable to the relevant time scale of the process of interest. Because of the substantial computational cost, however, plain MD simulations often have difficulty in obtaining sufficient statistics for converged estimates, requiring the use of more-advanced techniques. Central in many enhanced sampling methods is the definition of a small set of relevant degrees of freedom (collective variables) that are able to describe the transitions between different metastable states of the system. The harmonic linear discriminant analysis (HLDA) has been shown to be useful for constructing low-dimensional collective variables in various complex systems. Here, we apply HLDA to study the free-energy landscape of a monomeric protein around its native state. More precisely, we study the K-Ras protein bound to GTP, focusing on two flexible loops and on the region associated with oncogenic mutations. We perform microsecond-long biased simulations on the wild type and on G12C, G12D, G12 V mutants, describe the resulting free-energy landscapes, and compare our predictions with previous experimental and computational studies. The fast interconversion between open and closed macroscopic states and their similar thermodynamic stabilities are observed. The mutation-induced effects include the alternations of the relative stabilities of different conformational states and the introduction of many microscopic metastable states. Together, our results demonstrate the applicability of the HLDA-based protocol for the conformational sampling of multiple flexible regions in folded proteins.

Binding free energy predictions in host-guest systems using Autodock4. A retrospective analysis on SAMPL6, SAMPL7 and SAMPL8 challenges

We systematically tested the Autodock4 docking program for absolute binding free energy predictions using the host-guest systems from the recent SAMPL6, SAMPL7 and SAMPL8 challenges. We found that Autodock4 behaves surprisingly well, outperforming in many instances expensive molecular dynamics or quantum chemistry techniques, with an extremely favorable benefit-cost ratio. Some interesting features of Autodock4 predictions are revealed, yielding valuable hints on the overall reliability of docking screening campaigns in drug discovery projects.

Accurate absolute free energies for ligand-protein binding based on non-equilibrium approaches

The accurate calculation of the binding free energy for arbitrary ligand-protein pairs is a considerable challenge in computer-aided drug discovery. Recently, it has been demonstrated that current state-of-the-art molecular dynamics (MD) based methods are capable of making highly accurate predictions. Conventional MD-based approaches rely on the first principles of statistical mechanics and assume equilibrium sampling of the phase space. In the current work we demonstrate that accurate absolute binding free energies (ABFE) can also be obtained via theoretically rigorous non-equilibrium approaches. Our investigation of ligands binding to bromodomains and T4 lysozyme reveals that both equilibrium and non-equilibrium approaches converge to the same results. The non-equilibrium approach achieves the same level of accuracy and convergence as an equilibrium free energy perturbation (FEP) method enhanced by Hamiltonian replica exchange. We also compare uni- and bi-directional non-equilibrium approaches and demonstrate that considering the work distributions from both forward and reverse directions provides substantial accuracy gains. In summary, non-equilibrium ABFE calculations are shown to yield reliable and well-converged estimates of protein-ligand binding affinity.

SAMPL7 blind predictions using nonequilibrium alchemical approaches

In the context of the SAMPL7 challenge, we computed, employing a non-equilibrium (NE) alchemical technique, the standard binding free energy of two series of host-guest systems, involving as a host the Isaac's TrimerTrip, a Cucurbituril-like open cavitand, and the Gilson's Cyclodextrin derivatives. The adopted NE alchemy combines enhanced sampling molecular dynamics simulations with driven fast out-of-equilibrium alchemical trajectories to recover the free energy via the Jarzynski and Crooks NE theorems. The GAFF2 non-polarizable force field was used for the parametrization. Performances were acceptable and similar in accuracy to those we submitted for Gibb's Deep Cavity Cavitands in the previous SAMPL6 host-guest challenge, confirming the reliability of the computational approach and exposing, in some cases, some important deficiencies of the GAFF2 non-polarizable force field.

Non-equilibrium approach for binding free energies in cyclodextrins in SAMPL7: force fields and software

In the current work we report on our participation in the SAMPL7 challenge calculating absolute free energies of the host–guest systems, where 2 guest molecules were probed against 9 hosts-cyclodextrin and its derivatives. Our submission was based on the non-equilibrium free energy calculation protocol utilizing an averaged consensus result from two force fields (GAFF and CGenFF). The submitted prediction achieved accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${1.38}\,\hbox {kcal}/\hbox {mol}$$\end{document}1.38kcal/mol in terms of the unsigned error averaged over the whole dataset. Subsequently, we further report on the underlying reasons for discrepancies between our calculations and another submission to the SAMPL7 challenge which employed a similar methodology, but disparate ligand and water force fields. As a result we have uncovered a number of issues in the dihedral parameter definition of the GAFF 2 force field. In addition, we identified particular cases in the molecular topologies where different software packages had a different interpretation of the same force field. This latter observation might be of particular relevance for systematic comparisons of molecular simulation software packages. The aforementioned factors have an influence on the final free energy estimates and need to be considered when performing alchemical calculations.

Methodological uncertainties in drug-receptor binding free energy predictions based on classical molecular dynamics

Computational approaches are becoming an essential tool in modern drug design and discovery, with fast compound triaging using a combination of machine learning and docking techniques followed by molecular dynamics binding free energies assessment using alchemical techniques. The traditional MD-based alchemical free energy perturbation (FEP) method faces severe sampling issues that may limits its reliability in automated workflows. Here we review the major sources of uncertainty in FEP protocols for drug discovery, showing how the sampling problem can be effectively tackled by switching to nonequilibrium alchemical techniques.

Molecular Basis of SARS-CoV-2 Infection and Rational Design of Potential Antiviral Agents: Modeling and Simulation Approaches

The emergence in late 2019 of the coronavirus SARS-CoV-2 has resulted in the breakthrough of the COVID-19 pandemic that is presently affecting a growing number of countries. The development of the pandemic has also prompted an unprecedented effort of the scientific community to understand the molecular bases of the virus infection and to propose rational drug design strategies able to alleviate the serious COVID-19 morbidity. In this context, a strong synergy between the structural biophysics and molecular modeling and simulation communities has emerged, resolving at the atomistic level the crucial protein apparatus of the virus and revealing the dynamic aspects of key viral processes. In this Review, we focus on how in silico studies have contributed to the understanding of the SARS-CoV-2 infection mechanism and the proposal of novel and original agents to inhibit the viral key functioning. This Review deals with the SARS-CoV-2 spike protein, including the mode of action that this structural protein uses to entry human cells, as well as with nonstructural viral proteins, focusing the attention on the most studied proteases and also proposing alternative mechanisms involving some of its domains, such as the SARS unique domain. We demonstrate that molecular modeling and simulation represent an effective approach to gather information on key biological processes and thus guide rational molecular design strategies.

SAMPL7: Host-guest binding prediction by molecular dynamics and quantum mechanics

Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges provide routes to compare chemical quantities determined using computational chemistry approaches to experimental measurements that are shared after the competition. For this effort, several computational methods have been used to calculate the binding energies of Octa Acid (OA) and exo-Octa Acid (exoOA) host-guest systems for SAMPL7. The initial poses for molecular dynamics (MD) were generated by molecular docking. Binding free energy calculations were performed using molecular mechanics combined with Poisson-Boltzmann or generalized Born surface area solvation (MMPBSA/MMGBSA) approaches. The factors that affect the utility of the MMPBSA/MMGBSA approaches including solvation, partial charge, and solute entropy models were also analyzed. In addition to MD calculations, quantum mechanics (QM) calculations were performed using several different density functional theory (DFT) approaches. From SAMPL6 results, B3PW91-D3 was found to overestimate binding energies though it was effective for geometry optimizations, so it was considered for the DFT geometry optimizations in the current study, with single-point energy calculations carried out with B2PLYP-D3 with double-, triple-, and quadruple-ζ level basis sets. Accounting for dispersion effects, and solvation models was deemed essential for the predictions. MMGBSA and MMPBSA correlated better to experiment when used in conjunction with an empirical/linear correction.

Virtual Double-System Single-Box: A Nonequilibrium Alchemical Technique for Absolute Binding Free Energy Calculations: Application to Ligands of the SARS-CoV-2 Main Protease

In the context of drug-receptor binding affinity calculations using molecular dynamics techniques, we implemented a combination of Hamiltonian replica exchange (HREM) and a novel nonequilibrium alchemical methodology, called virtual double-system single-box, with increased accuracy, precision, and efficiency with respect to the standard nonequilibrium approaches. The method has been applied for the determination of absolute binding free energies of 16 newly designed noncovalent ligands of the main protease (3CL^pro) of SARS-CoV-2. The core structures of 3CL^pro ligands were previously identified using a multimodal structure-based ligand design in combination with docking techniques. The calculated binding free energies for four additional ligands with known activity (either for SARS-CoV or SARS-CoV-2 main protease) are also reported. The nature of binding in the 3CL^pro active site and the involved residues besides the CYS-HYS catalytic dyad have been thoroughly characterized by enhanced sampling simulations of the bound state. We have identified several noncongeneric compounds with predicted low micromolar activity for 3CL^pro inhibition, which may constitute possible lead compounds for the development of antiviral agents in Covid-19 treatment.

SAMPL7 TrimerTrip host-guest binding affinities from extensive alchemical and end-point free energy calculations

The prediction of host-guest binding affinities with computational modelling is still a challenging task. In the 7th statistical assessment of the modeling of proteins and ligands (SAMPL) challenge, a new host named TrimerTrip was synthesized and the thermodynamic parameters of 16 structurally diverse guests binding to the host were characterized. In the TrimerTrip-guest challenge, only structures of the host and the guests are provided, which indicates that the predictions of both the binding poses and the binding affinities are under assessment. In this work, starting from the binding poses obtained from our previous enhanced sampling simulations in the configurational space, we perform extensive alchemical and end-point free energy calculations to calculate the host-guest binding affinities retrospectively. The alchemical predictions with two widely accepted charge schemes (i.e. AM1-BCC and RESP) are in good agreement with the experimental reference, while the end-point estimates perform poorly in reproducing the experimental binding affinities. Aside from the absolute value of the binding affinity, the rank of binding free energies is also crucial in drug design. Surprisingly, the end-point MM/PBSA method seems very powerful in reproducing the experimental rank of binding affinities. Although the length of our simulations is long and the intermediate spacing is dense, the convergence behavior is not very good, which may arise from the flexibility of the host molecule. Enhanced sampling techniques in the configurational space may be required to obtain fully converged sampling. Further, as the length of sampling in alchemical free energy calculations already achieves several hundred ns, performing direct simulations of the binding/unbinding event in the physical space could be more useful and insightful. More details about the binding pathway and mechanism could be obtained in this way. The nonequilibrium method could also be a nice choice if one insists to use the alchemical method, as the intermediate sampling is avoided to some extent.

Interaction of hydroxychloroquine with SARS-CoV2 functional proteins using all-atoms non-equilibrium alchemical simulations

Using a combination of enhanced sampling molecular dynamics techniques and non-equilibrium alchemical transformations with full atomistic details, we have shown that hydroxychloroquine (HCQ) may act as a mild inhibitor of important functional proteins for SARS-CoV2 replication, with potency increasing in the series PLpro, 3CLpro, RdRp. By analyzing the bound state configurations, we were able to improve the potency for the 3CLpro target, designing a novel HCQ-inspired compound, named PMP329, with predicted nanomolar activity. If confirmed in vitro, our results provide a molecular rationale for the use of HCQ or of strictly related derivatives in the treatment of Covid-19.

SAMPL7 TrimerTrip host-guest binding poses and binding affinities from spherical-coordinates-biased simulations

Host-guest binding remains a major challenge in modern computational modelling. The newest 7th statistical assessment of the modeling of proteins and ligands (SAMPL) challenge contains a new series of host-guest systems. The TrimerTrip host binds to 16 structurally diverse guests. Previously, we have successfully employed the spherical coordinates as the collective variables coupled with the enhanced sampling technique metadynamics to enhance the sampling of the binding/unbinding event, search for possible binding poses and calculate the binding affinities in all three host-guest binding cases of the 6th SAMPL challenge. In this work, we report a retrospective study on the TrimerTrip host-guest systems by employing the same protocol to investigate the TrimerTrip host in the SAMPL7 challenge. As no binding pose is provided by the SAMPL7 host, our simulations initiate from randomly selected configurations and are proceeded long enough to obtain converged free energy estimates and search for possible binding poses. The calculated binding affinities are in good agreement with the experimental reference, and the obtained binding poses serve as a nice starting point for end-point or alchemical free energy calculations. Note that as the work is performed after the close of the SAMPL7 challenge, we do not participate in the challenge and the results are not formally submitted to the SAMPL7 challenge.

Upgraded AMBER Force Field for Zinc-Binding Residues and Ligands for Predicting Structural Properties and Binding Affinities in Zinc-Proteins

We developed a novel force field in the context of AMBER parameterization for glutamate and aspartate zinc(II)-binding residues. The interaction between the zinc ion and the coordinating atoms is represented by a spherical nonbonded parameterization. The polarization effect due to the zinc ion has been taken into account by redefining the atomic charges on the residues through accurate quantum mechanical calculations. The new zinc-binding ASP and GLU residues, along with the CYS and HIS zinc-binding residues, parameterized in a recent work [Macchiagodena M.;J. Chem. Inf. Model.2019, 59, 3803-3816], allow users to reliably simulate 96% of the Zn-proteins available in the Protein Data Bank. The upgraded force field for zinc(II)-bound residues has been tested performing molecular dynamics simulations with an explicit solvent and comparing the structural information with experimental data for five different proteins binding zinc(II) with GLU, ASP, HIS, and CYS. We further validated our approach by evaluating the binding free energy of (R)-2-benzyl-3-nitropropanoic acid to carboxypeptidase A using a recently developed nonequilibrium alchemical method. We demonstrated that in this setting it is crucial to take into account polarization effects also on the metal-bound inhibitor.

A remark on the efficiency of the double-system/single-box nonequilibrium approach in the SAMPL6 SAMPLing challenge

The alchemical nonequilibrium switching technique was one of several methods in the top tier of performance in the recent SAMPL6 SAMPLing challenge in both accuracy and efficiency. In this paper, in the context of nonequilibrium alchemical switching, we compare the efficiency of the double-system/single-box (DSSB) approach (used in the SAMPL6 challenges) to the standard single-system/double-box method (SSDB). Exploiting the Crooks theorem in a simple but effective test case, we analytically show that the DSSB approach is almost twice as efficient as SSDB for slow near-equilibrium switching but it gives basically no gain over the conventional SSDB approach when the variance of the work distribution exceeds few [Formula: see text], with the potential of producing artifacts and entanglements if not judiciously implemented.

SAMPL6 host-guest binding affinities and binding poses from spherical-coordinates-biased simulations

Host-guest binding is a challenging problem in computer simulation. The prediction of binding affinities between hosts and guests is an important part of the statistical assessment of the modeling of proteins and ligands (SAMPL) challenges. In this work, the volume-based variant of well-tempered metadynamics is employed to calculate the binding affinities of the host-guest systems in the SAMPL6 challenge. By biasing the spherical coordinates describing the relative position of the host and the guest, the initial-configuration-induced bias vanishes and all possible binding poses are explored. The agreement between the predictions and the experimental results and the observation of new binding poses indicate that the volume-based technique serves as a nice candidate for the calculation of binding free energies and the search of the binding poses.

SAMPL6 blind predictions of water-octanol partition coefficients using nonequilibrium alchemical approaches

In this paper, we compute, by means of a non equilibrium alchemical technique, the water-octanol partition coefficients (LogP) for a series of drug-like compounds in the context of the SAMPL6 challenge initiative. Our blind predictions are based on three of the most popular non-polarizable force fields, CGenFF, GAFF2, and OPLS-AA and are critically compared to other MD-based predictions produced using free energy perturbation or thermodynamic integration approaches with stratification. The proposed non-equilibrium method emerges has a reliable tool for LogP prediction, systematically being among the top performing submissions in all force field classes for at least two among the various indicators such as the Pearson or the Kendall correlation coefficients or the mean unsigned error. Contrarily to the widespread equilibrium approaches, that yielded apparently very disparate results in the SAMPL6 challenge, all our independent prediction sets, irrespective of the adopted force field and of the adopted estimate (unidirectional or bidirectional) are, mutually, from moderately to strongly correlated.

Solvation free energies via alchemical simulations: let's get honest about sampling, once more

Free energy perturbation (FEP) approaches with stratification have seen widespread and increasing use in computational studies of biologically relevant molecules. However, when the molecular systems are characterized by a complex conformational free energy landscape, the assessment of convergence remains a concern for many practitioners. The sampling problem in FEP has been authoritatively addressed in a recent perspective paper [D. Mobley, J. Comput.-Aided Mol. Des., 2012, 26, 93], incisively entitled "Let's get honest about sampling". Here, I return to the issue of sampling in the determination of the octanol-water partition coefficient for a synthetic precursor of kinase inhibitors that has been included in the recent extension of the SAMPL6 blind challenge of log P coefficients. I will show that even for this simple compound, whose conformational space is essentially dictated by two sp₃ rotable bonds connecting rigid planar units, canonical sampling using standard techniques can be surprisingly hard to achieve. I will also show how the conformational sampling problem can be effectively bypassed using unidirectional and bidirectional nonequilibrium work methods, reliably recovering the solvation energy with minimal methodological uncertainty.

Overview of the SAMPL6 host-guest binding affinity prediction challenge

Accurately predicting the binding affinities of small organic molecules to biological macromolecules 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 macromolecules 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 ten 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.