Mechanistic Investigation of Lysine-Targeted Covalent Inhibition of PI3Kδ via ONIOM QM:QM Computations

Dynamics Playing a Key Role in the Covalent Binding of Inhibitors to Focal Adhesion Kinase

Covalent kinase inhibitors (CKIs) have recently garnered considerable attention, yet the rational design of CKIs continues to pose a great challenge. In the discovery of CKIs targeting focal adhesion kinase (FAK), it has been observed that the chemical structure of the linkers plays a key role in achieving covalent targeting of FAK. However, the mechanism behind the observation remains elusive. In this work, we employ a comprehensive suite of advanced computational methods to investigate the mechanism of CKIs covalently targeting FAK. We reveal that the linker of an inhibitor influences the contacts between the warhead and residue(s) and the residence time in active conformation, thereby dictating the inhibitor's capability to bind covalently to FAK. This study reflects the complexity of CKI design and underscores the importance of considering the dynamic interactions and residence times for the successful development of covalent drugs.

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery

Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.