Dataset Information

Landscape of drug-resistance mutations in kinase regulatory hotspots.

ABSTRACT: More than 48 kinase inhibitors (KIs) have been approved by Food and Drug Administration. However, drug-resistance (DR) eventually occurs, and secondary mutations have been found in the previously targeted primary-mutated cancer cells. Cancer and drug research communities recognize the importance of the kinase domain (KD) mutations for kinasopathies. So far, a systematic investigation of kinase mutations on DR hotspots has not been done yet. In this study, we systematically investigated four types of representative mutation hotspots (gatekeeper, G-loop, αC-helix and A-loop) associated with DR in 538 human protein kinases using large-scale cancer data sets (TCGA, ICGC, COSMIC and GDSC). Our results revealed 358 kinases harboring 3318 mutations that covered 702 drug resistance hotspot residues. Among them, 197 kinases had multiple genetic variants on each residue. We further computationally assessed and validated the epidermal growth factor receptor mutations on protein structure and drug-binding efficacy. This is the first study to provide a landscape view of DR-associated mutation hotspots in kinase's secondary structures, and its knowledge will help the development of effective next-generation KIs for better precision medicine.

SUBMITTER: Kim P

PROVIDER: S-EPMC8138826 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Landscape of drug-resistance mutations in kinase regulatory hotspots.

Kim Pora P Li Hanyang H Wang Junmei J Zhao Zhongming Z

Briefings in bioinformatics 20210501 3

More than 48 kinase inhibitors (KIs) have been approved by Food and Drug Administration. However, drug-resistance (DR) eventually occurs, and secondary mutations have been found in the previously targeted primary-mutated cancer cells. Cancer and drug research communities recognize the importance of the kinase domain (KD) mutations for kinasopathies. So far, a systematic investigation of kinase mutations on DR hotspots has not been done yet. In this study, we systematically investigated four type ...[more]

PMID: 32510566

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Project description: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 IC50, K d, and K i). The performance of various web servers over BINDKIN shows that they cannot predict the binding affinities (ΔGs) of wild-type and mutant cases directly. Still, they could catch whether a mutation improves or worsens the ligand binding (ΔΔGs) where the highest Pearson's R correlation coefficient is reached by DSX-ONLINE over the K i dataset. When homology models are used instead of K i-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).

Dataset Information

Landscape of drug-resistance mutations in kinase regulatory hotspots.

Publications

Landscape of drug-resistance mutations in kinase regulatory hotspots.

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