Emergence of allosteric drug-resistance mutations: new challenges for allosteric drug discovery

PI3K inhibitors: review and new strategies

“…This argues for allosteric drugs. [27][28][29][30][31][32][33][34] Unfortunately, these efforts are yet to produce effective drugs. Notwithstanding, the encouraging successes with KRas4B, another hitherto undruggable oncogenic protein, where creative covalent allosteric drugs target KRas4B G12C , and discovery of new PI3Ka pockets, 27 breed cautious optimism.…”

Bcr-Abl Allosteric Inhibitors: Where We Are and Where We Are Going to

“…Summarizing, it is possible to recognize an Abl regulatory module (RM), constituted by the SH3, SH2, and N-cap domains, that is able to modulate KD activation by shifting from the inactive to active state [ 33 ]. When the RM docks at the back of the KD, thanks to the interactions of its SH2 and SH3 with C- and N-lobes, Abl is auto-inhibited by the penetration of the myristoyl group deep into its hydrophobic myristoyl pocket.…”

Discovery of cryptic allosteric sites using reversed allosteric communication by a combined computational and experimental strategy

“…The prerequisite for allosteric drug design is the precise identification of allosteric sites. However, most characterized allosteric sites were serendipitously discovered through exhaustive and tedious experimental approaches, such as high-throughput screening and alaninescanning mutagenesis 2,[14][15][16] . Computational tools, such as AlloFinder and Ohm can overcome the limitations associated with allosteric site identification 17,18 .…”

“…Computational tools, such as AlloFinder and Ohm can overcome the limitations associated with allosteric site identification 17,18 . However, the computational methods generate artifacts due to the use of potentially biased structural data for model training and non-physiological dummy atoms or ligands for site detection and docking 14,15,19,20 . Moreover, the detection of cryptic allosteric sites, which can expand the repertoire of allosteric drug discovery, is not possible using these computational methods.…”

“…Moreover, these experimental findings demonstrated the desirable predictive efficacy of our computational reversed allostery model, which represents a novel promising methodology for allosteric site identification and allosteric drug design. Previously, allosteric drugs were identified mainly through serendipitous discovery due to difficulties in the identification and characterization of their binding sites2,14,15,20 . The rapid development of computational tools to predict allosteric sites partially enabled overcoming these limitations17,[68][69][70] .…”