The question of how allostery works was posed almost 50 years ago. Since then it has been the focus of much effort. This is for two reasons: first, the intellectual curiosity of basic science and the desire to understand fundamental phenomena, and second, its vast practical importance. Allostery is at play in all processes in the living cell, and increasingly in drug discovery. Many models have been successfully formulated, and are able to describe allostery even in the absence of a detailed structural mechanism. However, conceptual schemes designed to qualitatively explain allosteric mechanisms usually lack a quantitative mathematical model, and are unable to link its thermodynamic and structural foundations. This hampers insight into oncogenic mutations in cancer progression and biased agonists' actions. Here, we describe how allostery works from three different standpoints: thermodynamics, free energy landscape of population shift, and structure; all with exactly the same allosteric descriptors. This results in a unified view which not only clarifies the elusive allosteric mechanism but also provides structural grasp of agonist-mediated signaling pathways, and guides allosteric drug discovery. Of note, the unified view reasons that allosteric coupling (or communication) does not determine the allosteric efficacy; however, a communication channel is what makes potential binding sites allosteric.

Driver mutations promote initiation and progression of cancer. Pharmacological treatment can inhibit the action of the mutant protein; however, drug resistance almost invariably emerges. Multiple studies revealed that cancer drug resistance is based upon a plethora of distinct mechanisms. Drug resistance mutations can occur in the same protein or in different proteins; as well as in the same pathway or in parallel pathways, bypassing the intercepted signaling. The dilemma that the clinical oncologist is facing is that not all the genomic alterations as well as alterations in the tumor microenvironment that facilitate cancer cell proliferation are known, and neither are the alterations that are likely to promote metastasis. For example, the common KRas^G12C driver mutation emerges in different cancers. Most occur in NSCLC, but some occur, albeit to a lower extent, in colorectal cancer and pancreatic ductal carcinoma. The responses to KRas^G12C inhibitors are variable and fall into three categories, (i) new point mutations in KRas, or multiple copies of KRAS G12C which lead to higher expression level of the mutant protein; (ii) mutations in genes other than KRAS; (iii) original cancer transitioning to other cancer(s). Resistance to adagrasib, an experimental antitumor agent exerting its cytotoxic effect as a covalent inhibitor of the G12C KRas, indicated that half of the cases present multiple KRas mutations as well as allele amplification. Redundant or parallel pathways included MET amplification; emerging driver mutations in NRAS, BRAF, MAP2K1, and RET; gene fusion events in ALK, RET, BRAF, RAF1, and FGFR3; and loss-of-function mutations in NF1 and PTEN tumor suppressors.

Without foreknowledge of the complete drug target information, development of promising and affordable approaches for effective treatment of human diseases is challenging. Here, we develop deepDTnet, a deep learning methodology for new target identification and drug repurposing in a heterogeneous drug–gene–disease network embedding 15 types of chemical, genomic, phenotypic, and cellular network profiles. Trained on 732 U.S. Food and Drug Administration-approved small molecule drugs, deepDTnet shows high accuracy (the area under the receiver operating characteristic curve = 0.963) in identifying novel molecular targets for known drugs, outperforming previously published state-of-the-art methodologies. We then experimentally validate that deepDTnet-predicted topotecan (an approved topoisomerase inhibitor) is a new, direct inhibitor (IC₅₀ = 0.43 μM) of human retinoic-acid-receptor-related orphan receptor-gamma t (ROR-γt). Furthermore, by specifically targeting ROR-γt, topotecan reveals a potential therapeutic effect in a mouse model of multiple sclerosis. In summary, deepDTnet offers a powerful network-based deep learning methodology for target identification to accelerate drug repurposing and minimize the translational gap in drug development.

The concept of allostery has evolved in the past century. In this Editorial, we briefly overview the history of allostery, from the pre-allostery nomenclature era starting with the Bohr effect (1904) to the birth of allostery by Monod and Jacob (1961). We describe the evolution of the allostery concept, from a conformational change in a two-state model (1965, 1966) to dynamic allostery in the ensemble model (1999); from multi-subunit (1965) proteins to all proteins (2004). We highlight the current available methods to study allostery and their applications in studies of conformational mechanisms, disease, and allosteric drug discovery. We outline the challenges and future directions that we foresee. Altogether, this Editorial narrates the history of this fundamental concept in the life sciences, its significance, methodologies to detect and predict it, and its application in a broad range of living systems.