Benchmarking in silico Tools for Cysteine pKa Prediction

Abstract

Accurate estimation of the pKas of cysteine residues in proteins could inform targeted approaches in hit discovery. The pKa of a targetable cysteine residue in a disease-related protein is an important physiochemical parameter in covalent drug dis- covery, as it influences the fraction of nucleophilic thiolate amenable to chemical protein modification. Traditional structure-based in silico tools are limited in their predictive accuracy of cysteine pKas relative to other titratable residues. Additionally, there are limited comprehensive benchmark assessments for cysteine pKa predictive tools. This raises the need for extensive assessment and evaluation of methods for cysteine pKa prediction. Here, we report the performance of several computational pKa methods, including single structure and ensemble-based approaches, on a diverse test set of experimental cysteine pKas retrieved from the PKAD data- base. The dataset consisted of 16 wildtype and 10 mutant proteins with experimentally measured cysteine pKa values. Our results highlight that these methods are varied in their overall predictive accuracies. Among the test set of wildtype proteins evaluated, the best method yielded a mean absolute error of 2.3 pK units highlighting the need for improvement of existing pKa methods for accurate cysteine pKa estimation. Given the limited accuracy of these methods, further development is needed before these approaches can be routinely employed to drive design decisions in early drug discovery efforts.

Accurately Predicting Protein pKa Values Using Nonequilibrium Alchemy

“…While our results and the recent work of others , underscore the p K a prediction accuracy attainable by MD-based free energy methods, the gap between prediction and experiment remains larger than the experimental error of 0.1–0.2 p K units . In the current work, we have identified two main sources contributing to the p K a prediction error: residue coupling and force-field parametrization.…”

“…Our finding underscores the importance of accurately parametrizing both the protonated and deprotonated forms of the amino acids and the sensitivity that relative free energy calculations can have to seemingly minor parametrization differences. Suggestive of this phenomenon was the recent demonstration that modification of the Amber14SB cysteine thiolate parametersto agree more closely with ab initio solvation datacould improve the p K a prediction accuracy by 0.5 p K units when combined with an MD-based approach . The use of polarizable force fields might also improve p K a estimates; however, recent work using Monte Carlo simulations with the Drude force field and a Poisson–Boltzmann continuum solvent model did not show a significantly improved prediction accuracy …”

“…One interesting point of investigation could be determining whether these modified charges resolve previously documented ion-overbinding problems 135 and conformational discrepancies in polyelectrolytes. 145 While our results and the recent work of others 58,146 underscore the pK a prediction accuracy attainable by MDbased free energy methods, the gap between prediction and experiment remains larger than the experimental error of 0.1− 0.2 pK units. 36 In the current work, we have identified two main sources contributing to the pK a prediction error: residue coupling and force-field parametrization.…”

Kinetics of the nitrate-mediated photooxidation of monocarboxylic acids in the aqueous phase

“…The pK a s used to calculate a for FA, GA, and PA were 3.75, 33 3.82, 52 and 2.50, 53 respectively. The addition of AN and H 2 SO 4 or NH 4 OH to the solution will increase the solution's ionic strength.…”

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery

“…A pK a study was done by Awoonor-Williams et al 49 on a diverse test set of experimental cysteine pK a 's retrieved from the PKAD database to test the performance of several computational pK a methods. This is important, since the fraction of nucleophilic thiolate amenable to chemical modification in a disease-related protein is influenced by the pK a of a targetable cysteine residue while traditional structurebased in silico tools are limited in their predictive accuracy of cysteine pK a 's relative to other titratable residues.…”