J Chem Inf Model - Efficient a priori identification of drug resistant mutations using Dead-End Elimination and MM-PBSA.

Tópicos

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Resumo

Active site mutations that disrupt drug binding are an important mechanism of drug resistance. Computational methods capable of predicting resistance a priori are poised to become extremely useful tools in the fields of drug discovery and treatment design. In this paper, we describe an approach to predicting drug resistance on the basis of Dead-End Elimination and MM-PBSA that requires no prior knowledge of resistance. Our method utilizes a two-pass search to identify mutations that impair drug binding while maintaining affinity for the native substrate. We use our method to probe resistance in four drug-target systems: isoniazid-enoyl-ACP reductase (tuberculosis), ritonavir-HIV protease (HIV), methotrexate-dihydrofolate reductase (breast cancer and leukemia), and gleevec-ABL kinase (leukemia). We validate our model using clinically known resistance mutations for all four test systems. In all cases, the model correctly predicts the majority of known resistance mutations.

Resumo Limpo

activ site mutat disrupt drug bind import mechan drug resist comput method capabl predict resist priori pois becom extrem use tool field drug discoveri treatment design paper describ approach predict drug resist basi deadend elimin mmpbsa requir prior knowledg resist method util twopass search identifi mutat impair drug bind maintain affin nativ substrat use method probe resist four drugtarget system isoniazidenoylacp reductas tuberculosi ritonavirhiv proteas hiv methotrexatedihydrofol reductas breast cancer leukemia gleevecabl kinas leukemia valid model use clinic known resist mutat four test system case model correct predict major known resist mutat

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