How Far Are We from the Rapid Prediction of Drug Resistance Arising Due to Kinase Mutations?

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ID: 274817
2021
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Abstract
In all living organisms, protein kinases regulate various cell signaling events through phosphorylation. The phosphorylation occurs upon transferring an ATP's terminal phosphate to a target residue. Because of the central role of protein kinases in several proliferative pathways, point mutations occurring within the kinase's ATP-binding site can lead to a constitutively active enzyme, and ultimately, to cancer. A select set of these point mutations can also make the enzyme drug resistant toward the available kinase inhibitors. Because of technical and economical limitations, rapid experimental exploration of the impact of these mutations remains to be a challenge. This underscores the importance of kinase-ligand binding affinity prediction tools that are poised to measure the efficacy of inhibitors in the presence of kinase mutations. To this end, here, we compare the performances of six web-based scoring tools (DSX-ONLINE, KDEEP, HADDOCK2.2, PDBePISA, Pose&Rank, and PRODIGY-LIG) in assessing the impact of kinase mutations on their interactions with their inhibitors. This assessment is carried out on a new structure-based BINDKIN benchmark we compiled. BINDKIN contains wild-type and mutant structure pairs of kinase-inhibitor complexes, together with their corresponding experimental binding affinities (in the form of IC, , and ). The performance of various web servers over BINDKIN shows that they cannot predict the binding affinities (Δs) of wild-type and mutant cases directly. Still, they could catch whether a mutation improves or worsens the ligand binding (ΔΔs) where the highest Pearson's correlation coefficient is reached by DSX-ONLINE over the dataset. When homology models are used instead of -associated crystal structures, DSX-ONLINE loses its predictive capacity. These results highlight that there is room to improve the available scoring functions to estimate the impact of protein kinase point mutations on inhibitor binding. The BINDKIN benchmark with all related results is freely accessible online (https://github.com/CSB-KaracaLab/BINDKIN).
Reference Key
erguven2021howacs Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Erguven, Mehmet;Karakulak, Tülay;Diril, M Kasim;Karaca, Ezgi;
Journal ACS omega
Year 2021
DOI
10.1021/acsomega.0c04672
URL
Keywords
mediterranean sea microsatellites mitochondrial dna ancient dna xiphias gladius effective population size

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