# ﻿(B) Compound 1 and the corresponding simulated annealing composite omit electron denseness map contoured at 3 is shown (mFo C DFc, green mesh)

﻿(B) Compound 1 and the corresponding simulated annealing composite omit electron denseness map contoured at 3 is shown (mFo C DFc, green mesh). mutations at the early stages of the design process. Graphical Abstract Pisa et al. identifies how engineered point mutations in protein active sites can be used to forecast the binding modes of chemical inhibitors. These data can guidebook inhibitor optimization and may determine cognate resistance-conferring mutations at the start of the inhibitor design process. Intro Enzyme active sites are common binding sites for chemical inhibitors, as compounds can mimic substrates or co-factors to compete for occupancy (Copeland, 2013). Active sites are typically comprised of conserved structural motifs and amino acid sequences and their overall steric and stereoelectronic features can be related across enzymes within a protein family (Wendler et al., 2012, Endicott et al., 2012). For some protein families, such as kinases, a wealth of high-resolution structural data for how different chemical scaffolds interact with residues in conserved active sites has enabled the design of selective chemical inhibitors (Ferguson and Gray, 2018). However, the relatively low resolution Dihydroactinidiolide (3C4 ?) of constructions for many proteins, including users of AAA (ATPases associated with varied cellular activities) family (Erzberger and Berger, 2006), can limit their use for rational inhibitor design (Davis et al., 2008) and additional approaches are needed to determine the key relationships determining inhibitor potency and specificity (Erlanson et al., 2019). Proteins in the AAA (ATPases associated with varied cellular activities) family carry out critical tasks in various cellular processes including DNA unwinding and replication, protein unfolding or membrane remodelling (Bleichert et al., 2017; McCullough et al., 2018; vehicle den Growth and Meyer, 2018). For some AAA proteins, chemical inhibitors have been recognized by screening compound libraries (Anderson et al., 2015; Chou et al., 2011; Firestone et al., 2012; Kawashima et al., 2016; Magnaghi et al., 2013). In most cases, the inhibitor binding sites have been mapped to the Dihydroactinidiolide AAA website, the core enzymatic module of AAA proteins (Wendler et al., 2012), either in the active site (Anderson et al., 2015; Cupido et al., 2019; Magnaghi et al., 2013) in an allosteric site (Magnaghi et al., 2013, Banerjee et al., 2016; Pohler et al., 2018). Structural models for some inhibitor-bound AAA proteins are also now available (Banerjee et al., 2016; Boyaci et al., 2016; Pisa et al., 2019; Tang et al., 2019). However, for many AAA proteins the key inhibitor-target interactions needed for the design of selective chemical inhibitors are not known. We have recently focused on spastin, a microtubule-severing AAA protein whose functions have been linked to several cellular processes including nuclear envelope reformation and cytokinesis (Connell et al., 2009; Vietri et al., 2015). In addition, obstructing spastin function NFATC1 offers been shown to reduce amyloid- toxicity inside a model for Alzheimers disease (Zempel et al., 2013). Consequently, chemical inhibitors would be important tools to probe spastin functions in normal physiology and disease. We recently designed spastazoline, a potent and selective inhibitor of spastin (Cupido et al., 2019). To design this pyrazolylpyrrolopyrimidine-based inhibitor, we analyzed compound activity against biochemically active mutant alleles of spastin. We reasoned that mutant alleles that alter the potency of compounds would reveal key compound-target relationships and guide the selection of powerful inhibitor-protein binding models. From a collection of heterocyclic scaffolds that could mimic key hydrogen-bonding interactions made by adenine in the AAA active site, we recognized a pyrazolyl-based scaffold. Screening this compound against wild-type and mutant spastin alleles exposed key interactions that we used to rank order different solutions from computational docking. We used the selected inhibitor-spastin binding model to design modifications of the core scaffold and generated spastazoline, the potent and selective inhibitor of spastin (Cupido et al., 2019). Structural models we generated by X-ray crystallography confirmed the expected binding models (Pisa et al., 2019). However, it remains unclear if our approach, which we now Dihydroactinidiolide name RADD (for Resistance Analysis During Design), can be used to determine binding site relationships of inhibitors based on different chemical scaffolds and if the target-binding modes we forecast are accurate. Here, we focus on applying RADD to diaminotriazole-based compounds, which are chemically unrelated to spastazoline. Screening compound activity against wild-type and mutant spastin alleles recognized important relationships Dihydroactinidiolide that contribute to inhibitor binding. Our approach also indicated that a more potent derivative binds spastin in a distinct pose, essentially oriented ~180 relative to the starting compound. High-resolution X-ray.