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

Presenter

Mehmet Ergüven

Mehmet Ergüven finished his Bachelor’s studies in the field of protein biochemistry (Department of Biochemistry, Ege University, Izmir) in 2016. He then carried out his Master’s studies in Izmir Biomedicine and Genome Center in the field of cell biology and computational structural biology. After finishing his Master’s in 2019, he continued studying in the same place as a research assistant for one year. He is currently doing his PhD studies in the field of chemoenzymatic synthesis (Cells in Motion graduate School, University of Münster, Institute for Biochemistry, Münster).

Abstract

Many protein kinases act in proliferative pathways. Consequently, point mutations occurring within the kinase’s ATP-binding site can lead to a constitutively active or drug-resistant enzyme, and ultimately, to cancer. Because of technical and economical limitations, rapid experimental exploration of the impact of such mutations remains to be a challenge. This underscores the importance of protein−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 crystal structure pairs of kinase−inhibitor complexes, together with their corresponding experimental binding affinities (in the form of IC50, Kd, and Ki). 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, few of the web servers could catch whether a mutation improves or worsens the ligand binding (ΔΔGs), with Ki being the most predictable descriptor and DSX-ONLINE being the most accurate predictor. When homology models are used instead of Ki-associated crystal structures, DSX-ONLINE loses its predictive capacity. The results highlight that there is room to improve the available scoring functions to estimate the impact of protein kinase point mutations on inhibitor binding.

Date: July 16th, 2021 – 6:00 PM (GMT+3)

Language: English

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