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Controlling Induced Folding Through Wrapping Drug Design

  • Ariel Fernández Stigliano
Chapter

Abstract

The conformational plasticity of protein kinases often mars the success of structure-based design of kinase inhibitors. This is because kinases and other signaling proteins can undergo unpredictable induced folding or structural adaptation upon ligand binding. Thus, dynamic information must be incorporated into the designer’s analysis. The conformation of the protein chain within the target/drug complex often differs significantly from the conformation of the free (apo) form of the kinase, and the induced conformational change is usually unexpected and unpredictable. In fact, the induced folding problem is every bit as difficult as the protein folding problem, whose arduous solution was presented in Chap. 3. This wild conformational behavior may be the main reason for the modest interest in rational drug design when targeting regions with high disorder propensity, such as the activation loop (or even the nucleotide-binding loop). Yet, the activation loop is the structural region that presents the largest amino acid variability within the superfamily and thus constitutes an attractive target to control specificity. In this chapter we advocate for a strategy to target flexible regions, offering a way to control the induced folding and turn it into a selectivity-promoting feature. Drugs designed to wrap disordered regions in the target may be used to steer induced folding in very specific ways, i.e., by inducing the formation of specific dehydrons. The results surveyed in this chapter herald the paradigmatic design concept of “wrapping drugs for structurally adaptable targets.” The wrapping-induced folding concept is illustrated by redesigning the anticancer drug imatinib in order to redirect its affinity toward a floppy region in JNK1, which constitutes an important target in the treatment of ovarian cancer.

Keywords

Ovarian Cancer Chronic Myeloid Leukemia Epithelial Ovarian Cancer Activation Loop Packing Defect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.National Research Council–CONICETBuenos AiresArgentina
  2. 2.Former Karl F. Hasselmann Endowed Chair Professor of BioengineeringRice UniversityHoustonUSA

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