Results
Findings about MCCE and its settings
How does changing MCCE's parameters change accuracy? (under construction)
MCCE offers a wide range of customizable parameter that influence the accuracy of its pKa predictions. Some major options include varying the internal dielectric constants, numerical Poisson-Boltzmann solvers, Van der Waal functions, and explicit crystal water inclusion/removal (wet/dry). Each of these options or combinatoric assortments of these options can significantly impact MCCE's free energy model, and in turn, affects its pKa predictions. In this study, we systematically evaluate how these parameter choices affect MCCE's calculated pKa values against experimental benchmark values.
Here, we use a set of 36 PDB files sourced from RCSB.org, listed HERE. These PDB files were chosen for their varying sizes, as well as the amount of experimental data available for their residue pKa values. You can find the sources for the experimental pKas in this file: pkdbv1_WT_pkas.csv (change this later to be a citation probably)
Accuracy Criteria
Our benchmark dataset consists of 36 PDB files comprising a total of 1,587 residues, of which 425 have experimentally verified pKa values. These serve as the reference for evaluating the accuracy of MCCE’s pKa predictions. To perform this evaluation, we developed an in-house program that runs parallel MCCE simulations across the 36 structures and compares the predicted pKa values to the experimental values.
We define pKa Accuracy as the percentage of predicted pKa values that fall within ±1 unit of the corresponding experimental values. Additionally, we report the root-mean-square deviation (RMSD), noting that it is sensitive to outliers; where appropriate, we assess how removing outliers affects this metric.
The data presented below were generated using the NGPB Poisson–Boltzmann solver with an internal dielectric constant of 8 and our modified, unscaled van der Waals function. Under these conditions, we observed a pKa Accuracy of 82.35% and an RMSD of 0.88.
In this graph the experimental pKa values are on the X-axis and the MCCE-predicted pKa values on the Y-axis. This particular run incorporates extra values, which are residue-type-specific adjustments applied after the initial pKa calculations. These adjustments shift all predicted pKas of a given residue type uniformly up or down to reduce systematic bias. However, it's important to note that while extra values can correct for bias, they do not address the variance within a residue group relative to experimental values.
For comparison, the following graph shows the same dataset without applying extra values. Here, we observe the pKa accuracy was reduced by 4% to 78.35% and an inflated RMSD of 0.92.
Poisson-Boltzmann Solvers
MCCE4 is currently capable of switching between three different Poisson-Boltzmann solvers: ZAP (OpenEye), Delphi, and Next-Generation Poisson (NGPB created by partners in the CONCEPT Lab @ Istituto Italiano di Tecnologia (ITT) in conjunction with Gunner Lab).
Need Figure(s): (include RMSD w and w/o outliers)
Here, we see the pKa accuracy of our three solvers on our testing set. These are dry runs, using D=8, and the newer VDW equations (see below). We have removed all "extra" terms for these graphs. This highlights the consistency of MCCE between the different solvers.
Dielectric Constant
MCCE output shows significant variance depending on the inner dielectric constant (ε). Below, we compare the optimized results from two dry NGPB runs: the left with ε=4 , and the right ε=8. Both runs use unscaled Van der Waals interactions.
The different residue groups are distributed similarly, but the E=4 runs experience a greater variance, especially for the 425 residues with experimental data. Directly comparing these two runs across all 1,587 residues, we find an average delta mean of -.02, and an average absolute delta mean of .42. Let's look at this direct comparison:
Wet/Dry Runs
We observed little change in pKa calculation controlling for whether waters were removed from a PDB file.
In this graph, we see minimal changes in pKa calculation between wet and dry runs of NGPB for D=8. For comparison's sake, NGPB has a pKa accuracy of 98.05%, NGPB for D=4 has 96.98%, and Delphi for D=4 has 96.85%.