Benchmarking In Silico Tools for Cysteine p K a Prediction

Accurate estimation of the p 's of cysteine residues in proteins could inform targeted approaches in hit discovery. The p of a targetable cysteine residue in a disease-related protein is an important physiochemical parameter in covalent drug discovery, as it influences the fraction of nucleophilic thiolate amenable to chemical protein modification. Traditional structure-based tools are limited in their predictive accuracy of cysteine p 's relative to other titratable residues. Additionally, there are limited comprehensive benchmark assessments for cysteine p predictive tools. This raises the need for extensive assessment and evaluation of methods for cysteine p prediction. Here, we report the performance of several computational p methods, including single-structure and ensemble-based approaches, on a diverse test set of experimental cysteine p 's retrieved from the PKAD database. The dataset consisted of 16 wildtype and 10 mutant proteins with experimentally measured cysteine p 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 (MOE) yielded a mean absolute error of 2.3 p units, highlighting the need for improvement of existing p methods for accurate cysteine p 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.