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.
Disruption of the YAP-TEAD protein-protein interaction is an attractive therapeutic strategy for oncology to suppress tumour progression and cancer metastasis. YAP binds to TEAD at a large flat binding interface (~3500 2) devoid of a well-defined druggable pocket, so it has been difficult to design low molecular weight compounds to abrogate this protein-protein interaction directly. Recently, work by Furet and coworkers (ChemMedChem 2022, DOI: 10.1002/cmdc.202200303) reported the discovery of the first class of small molecules able to efficiently disrupt the transcriptional activity of TEAD by binding to a specific interaction site of the YAP-TEAD binding interface. Using high-throughput in silico docking and structure-based drug design, they identified a virtual screening hit from a hot-spot derived from their previously rationally designed peptidic inhibitor. Given advances in rapid high-throughput screening and rational approaches to peptidic ligand discovery for challenging targets, we analyzed the pharmacophore features involved in transferring from their peptidic to small molecule inhibitor that could enable small molecule discovery for such targets. Here, we show that pharmacophore analysis augmented by solvation analysis of molecular dynamics trajectories can guide the designs while binding free energy calculations provide greater insight into the binding conformation and energetics accompanying the association event. The computed binding free energy estimates agree with experimental findings and offer useful insight into structural determinants that influence ligand binding to the TEAD interaction surface, even for such a shallow binding site. In general, our results provide rationale for the structure-based design efforts in compound optimization that led to a significant gain in potency among the first class of potent small molecule YAP:TEAD protein-protein inhibitors.
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