Recent developments in structure-based drug design.

Structure-based design and optimization of potent renin inhibitors on 5- or 7-azaindole-scaffolds

Structure-based drug design has played a role in the discovery of many drugs from many different disease areas, and it is now arguably an essential contributor to addressing the need to improve research and development productivity faced by the pharmaceutical industry. The purpose of this review is to highlight the impact of protein structures solved by X-ray and NMR techniques, as well as other techniques such as isothermal titration calorimetry, on structure-based discovery. The importance of these methods in drug discovery will be underscored by examples of drugs approved up until the end of 2009, which were discovered utilising structural information. These examples will highlight that structure-based drug design has moved on from just using structural knowledge to optimise potency to using it also with selectivity, pharmacokinetic and pharmaceutical properties in mind. Following on from this, a medicinal chemist's perspective aims to highlight the benefits and advantages of structure-based drug design but also to provide an opinion on the pitfalls and hype surrounding this technology. In particular, this section will draw on examples to highlight its impact and limitations on assessing druggability, as well as factors to consider when using structure-based drug design in lead generation and the optimisation of leads for potency, selectivity and pharmacokinetic properties. Finally, some forward looking thoughts will be proposed with regard to the future of structure-based drug design and where emphasis could be placed to increase the impact of this approach on drug discovery productivity.

This review provides a detailed analysis of the use of virtual library screening (VLS) in the drug discovery process. The first part is intended as a larger overview of the integrated VLS process. Small molecule and target macromolecule considerations will be described separately and will be subsequently integrated in a discussion of docking, scoring and evaluation. The second half of the review will focus on recent case studies that use VLS as part of an integrated drug discovery program. The case studies will illustrate the range of possible targets in VLS, provide an account of inclusive methodology and reveal the expectations for realistic goals. Recent efforts provide compelling evidence that VLS is successful when practiced in an integrated fashion involving synthetic, structural and computational expertise. Keywords: virtual screening, structure-based drug design, library design, drug discovery, diversity, filtering, ligand binding, docking

Structure-based design and discovery of potent and selective lysine-specific demethylase 1 (LSD1) inhibitors

We disclose herein optimization efforts around the novel, highly potent non-nucleoside adenosine deaminase (ADA) inhibitor, 1-[(R)-1-hydroxy-4-(6-(3-(1-methylbenzimidazol-2-yl)propionylamino)indol-1-yl)-2-butyl]imidazole-4-carboxamide 1 (K(i)= 7.7 nM), which we recently reported. Structure-based drug design (SBDD) utilizing the crystal structure of the 1/ADA complex was performed in order to obtain structure-activity relationships (SAR) for this type of compound rationally and effectively. To utilize the newly formed hydrophobic space (F2), replacement of the benzimidazole ring of 1 with a n-propyl chain (4b) or a simple phenyl ring (4c) was tolerated in terms of binding activity, and the length of the methylene-spacer was shown to be optimal at two or three. Replacement of an amide with an ether as a linker was also well tolerated in terms of binding activity and moreover improved the oral absorption (6a and 6b). Finally, transformation of indol-1-yl to indol-3-yl resulted in discovery of a novel highly potent and orally bioavailable ADA inhibitor, 1-[(R)-4-(5-(3-(4-chlorophenyl)propoxy)-1-methylindol-3-yl)-1-hydroxy-2-butyl]imidazole-4-carboxamide 8c.

Structure-based drug design is emerging as one of the key components in drug discovery, with many approved drugs tracing, at least part of their origins, to the use of structural information from X-ray, NMR, surface plasmon resonance, differential thermal denaturation, fluorescence polarization and other techniques for analysis of protein targets and their ligand-bound complexes. Furthermore, in silico structure-based drug design approach has enabled millions of possible structures for a given protein sequence to be evaluated rapidly helping to fast forward drug discovery as well as reduce drug discovery costs. Structure-based drug design is now arguably an essential contributor to addressing the need to improve research and development productivity faced by the pharmaceutical industry. The purpose of this meeting is to highlight the impact of the intersection of structural biology with chemistry and biology particularly on how the structures of relevant drug targets can serve as a starting point for drug design and development and provide the maximal synergy between target validation, structure determination, and hit-to-lead development. Some thoughts will be proposed with regard to the future of structure-based drug design and where emphasis could be placed to further increase the utilization of this approach on drug discovery.

Signal transducers and activators of the transcription (STAT) family is composed of seven structurally similar and highly conserved members, including STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6. The STAT3 signaling cascade is activated by upstream kinase signals and undergoes phosphorylation, homo-dimerization, nuclear translocation, and DNA binding, resulting in the expression of target genes involved in tumor cell proliferation, metastasis, angiogenesis, and immune editing. STAT3 hyperactivation has been documented in a number of tumors, including head and neck, breast, lung, liver, kidney, prostate, pancreas cancer, multiple myeloma, and acute myeloid leukemia. Drug discovery is a timeconsuming and costly process; it may take ten to fifteen years to bring a single drug to the market. Machine learning algorithms are very fast and effective and commonly used in the field, such as drug discovery. These algorithms are ideal for the virtual screening of large compound libraries to classify molecules as active or inactive. The present work aims to perform machine learning-based virtual screening for the STAT3 drug target. Machine learning models, such as k-nearest neighbor, support vector machine, Gaussian naïve Bayes, and random forest for classifying the active and inactive inhibitors against a STAT3 drug target, were developed. Ten-fold cross-validation was used for model validation. Then the test dataset prepared from the zinc database was screened using the random forest model. A total of 20 compounds with 88% accuracy was predicted as active against STAT3. Furthermore, these twenty compounds were docked into the active site of STAT3. The two complexes with good docking scores as well as the reference compound were subjected to MD simulation. A total of 100ns MD simulation was performed. Compared to all other models, the random forest model revealed better results. Compared to the standard reference compound, the top two hits revealed greater stability and compactness. In conclusion, our predicted hits have the ability to inhibit STAT3 overexpression to combat STAT3-associated diseases.

Huntington's disease (HD) is a lethal neurodegenerative disorder for which no cure is available yet. It is caused by abnormal expansion of a CAG triplet in the gene encoding the huntingtin protein (Htt), with consequent expansion of a polyglutamine repeat in mutated Htt (mHtt). This makes mHtt highly unstable and aggregation prone. Soluble mHtt is linked to cytotoxicity and neurotoxicity, whereas mHtt aggregates are thought to be neuroprotective. While Htt and mHtt are ubiquitously expressed throughout the brain and peripheral tissues, HD is characterized by selective degradation of the corpus striatum, without notable alterations in peripheral tissues. Screening for mRNAs preferentially expressed in rodent striatum led to the discovery of a GTP binding protein homologous to Ras family members. Due to these features, the newly discovered protein was termed Ras Homolog Enriched in Striatum (RHES). The aetiological role of RHES in HD has been ascribed to its small ubiquitin-like modifier (SUMO)-E3 ligase function. RHES sumoylates mHtt with higher efficiency than wild-type Htt, thereby protecting mHtt from degradation and increasing the amounts of the soluble form. Although RHES is an attractive target for HD treatment, essential information about protein structure and function are still missing. With the aim of investigating RHES 3D structure and function, bioinformatic analyses and molecular modelling have been performed in the present study, based on which, RHES regions predicted to be involved in the interaction with mHtt or the SUMO-E2 ligase Ubc9 have been identified. These regions have been used to design peptides aimed at inhibiting RHES interactions and, therefore, mHtt sumoylation; in turn, these peptides will be used to develop small molecule inhibitors by both rational design and virtual screening of large compound libraries. Once identified, RHES sumoylation inhibitors may open the road to the development of therapeutic agents against the severe, and currently untreatable, HD.

Conventional efforts relying on high-throughput physical and virtual screening of large compound libraries have failed to yield high-efficiency chemical probes for many of the 48 human nuclear receptors. Here, we investigated whether disulfide-trapping, an approach new to nuclear receptors, would provide effective lead compounds targeting human liver receptor homolog 1 (hLRH-1, NR5A2). Despite the fact that hLRH-1 contains a large ligand binding pocket and binds phospholipids with high affinity, existing synthetic hLRH-1 ligands are of limited utility due to poor solubility, low efficacy or significant off-target effects. Using disulfide-trapping, we identified a lead compound that conjugates with remarkably high-efficiency to a native cysteine residue (Cys346) lining the hydrophobic cavity in the ligand binding domain of hLRH-1. Guided by computational modeling and cellular assays, the lead compound was elaborated into ligands PME8 and PME9 that bind hLRH-1 reversibly (no cysteine reactivity) and increase hLRH-1 activity in cells. When compared with the existing hLRH-1 synthetic agonist RJW100, both PME8 and PME9 showed comparable induction of the LRH-1 dependent target gene CYP24A1 in human HepG2 cells, beginning as early as 3 h after drug treatment. The induction is specific as siRNA-mediated knock-down of hLRH-1 renders both PME8 and PME9 ineffective. These data show that PME8 and PME9 are potent activators of hLRH-1 and suggest that with further development this lead series may yield useful chemical probes for manipulating LRH-1 activity in vivo.

The rates of type 2 diabetes (T2D) are rising to epidemic proportions in the US and worldwide. While current T2D medications are efficacious, significant side effects have limited their use and availability. Our laboratory has discovered that abscisic acid (ABA) exerts anti-diabetic effects, in part, by activating peroxisome proliferator-activated receptor γ (PPAR γ). However, since ABA does not bind to the ligand-binding domain (LBD) of PPAR γ, the mechanism of activation of PPAR γ by ABA remains unknown. Lanthionine synthetase component C-like protein 2 (LANCL2) was predicted to be a novel target for the binding and signaling of ABA in human granulocytes and rat insulinoma cells. The goal of this study was to determine whether LANCL2 is a molecular target of ABA and other PPAR γ agonists. To this end we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of LANCL1 as a template. Our molecular docking studies predicted that ABA and other PPAR γ agonists (e.g., rosiglitazone and pioglitazone) share a binding site on the surface of LANCL2. The identification of a binding site for PPAR γ agonists will facilitate the high-throughput virtual screening of large compound libraries and may shed new light on alternative mechanisms of PPAR γ activation.

Recent developments in artificial intelligence have transformed the fragment-based drug design (FBDD), changing traditional approaches to drug discovery. Machine learning and deep learning algorithms now enable rapid exploration of vast chemical spaces, precise prediction of fragment properties, and optimization of binding interactions. The use of AI-driven methods with FBDD has enhanced virtual library screening efficiency, improved hit identification accuracy, and accelerated the fragment-to-lead optimization process. Deep generative models and physics-informed neural networks have shown remarkable capabilities in designing vast fragment libraries and predicting their physicochemical properties. Graph neural networks and reinforcement learning algorithms have proven particularly effective in binding affinity prediction and fragment elaboration methods. The combination of AI technologies with experimental methods, including X-ray crystallography, NMR spectroscopy, and surface plasmon resonance, has established new paradigms in structure-based drug design. Success stories in developing kinase inhibitors and targeting protein-protein interactions highlight the practical impact of AI-guided FBDD. These AI-enabled virtual library screening helps in reducing drug discovery timelines and improve success rates in lead optimization

Developing methods to incorporate protein flexibility into structure-based drug design is an important challenge. Our approach uses multiple protein structures (MPS) to create a receptor-based pharmacophore model of the desired target. We have previously demonstrated the success of the method by applying it to human immunodeficiency virus-1 protease (HIV-1p). Our models, based on an apo structure, discriminated known HIV-1p inhibitors from druglike inactive compounds and also accurately identified bound conformations of known inhibitors. Here, we test the method by applying it to all three unbound crystal structures of HIV-1p. We have also improved our method with denser probe mapping of the binding site and refined our selection criteria for pharmacophore elements. Our improved protocol has led to the development of a consistent 8-site pharmacophore model for HIV-1p, which is independent of starting structure, and a robust MPS pharmacophore method that is more amenable to automation.

Discovery of novel inhibitors of phosphodiesterase 4 with 1-phenyl-3,4-dihydroisoquinoline scaffold: Structure-based drug design and fragment identification.

Computational approaches are widely used to help discover and develop new drugs, in particular, to understand how these molecules interact with their biomolecular targets. There are well-optimised and validated force field parameters available to describe interactions of common biomolecules, such as proteins, lipids and nucleotides. However, these force fields are not designed to represent heteromolecular ligands such as substrates, inhibitors, co-factors and potential drug molecules. Errors in parameters may result in incorrect predictions of ligand structure, orientation and conformation, which in turn can lead to the failure of computational drug design efforts. With over 25% of all the structures in the Protein Data Bank (PDB) containing ligands and over a million ligand molecules of potential interest in drug design in other databases, ligand validation and parameterisation represents a significant scientific challenge. While several automated parameterisation protocols have been proposed to generate the parameters, none of the current procedures are properly validated. As a response to the high demand for interaction parameters for ligands compatible with the GROMOS force field, a web accessible Automated force field Topology Builder (ATB; http://atb.uq.edu.au/) and Repository was developed. The ATB and Repository is intended to facilitate the development of molecular force fields and generates parameters that can be used in X-ray refinement, structure-based drug design and study of biomolecule-ligand complexes. In this thesis, molecular dynamics (MD) simulations were used to calculate the thermodynamic properties for the validation and refinement of ATB force fields parameters. A fully automated validation protocol of the ATB force field parameters for small organic molecules based on thermodynamic and structural information was developed and incorporated into the ATB. A novel integration and convergence protocol which increases the efficiency of the TI method for free energy calculations was also proposed. The validation has shown good overall agreement between the experimental and computed values and indicated problematic functional groups with parameters to be refined. The generation of parameters for novel molecules that are compatible with a given biomolecular force field can be tedious, time-consuming and error-prone. Here, a novel method to refine parameters in classical force fields in an automated manner that can be extended to the parameterisation of all other atom types is also presented. Single-step perturbation protocols were developed for small halogenated molecules to establish alternative van der Waals parameters which describe the interactions of these molecules with high precision. Parameters were successfully refined against experimental hydration free energies, densities and heats of vaporisation. As a result, alternative van der Waals interactions parameters for chlorine and bromine were proposed. Properties calculated with these proposed parameters closely matched the experiment data. The quality of the ATB force field parameters in validating the X-ray structures and investigating binding of ligands to the protein endothiapepsin was also investigated. Analysis of the MD trajectories showed that the protein structure remained relatively stable after 10 ns of simulations. The results for the validation of the ligands were mixed. While in some cases the simulations reproduced the binding mode and the interactions between the protein and the ligand with high accuracy suggesting the ATB parameters performed well, in other cases the complexes were unstable. This was despite the fact that the electron density for all ligands was well defined and the parameters for the ligand were generated using the same procedure. Endothiapepsin contains a catalytic dyad consisting of two aspartates that are generally assumed to share a proton. We demonstrated that the protonation of both the ligand and the residues with the binding site were critical to the ability to reproduce the crystal complex. To conclude, the work presented in this thesis provided insight into the development and validation of the force field parameters for drug-like molecules and their applications in structure-based drug design.

INTRODUCTION. Malignant glioma is one of the most lethal diseases in adulthood and remains refractory to the current therapies including chemoradiotherapies. The cancer stem cell theory dictates that brain tumor stem-like cells (BTSC) account for such intractability of malignant glioma. Recently, several molecularly-targeted therapies have demonstrated a varying degree of success in treatment of cancers including leukemia and breast cancer. In this study, we sought to determine the key kinases that regulate survival, self-renewal, and proliferation of BTSC, and to design novel small molecules by using the structure-based computantional modeling system. RESULTS. With the neurosphere cultures derived from our glioma patients, we identified 3 kinases that play vital roles in survival and/or proliferation of BTSC in vitro; Fibroblast Growth Factor Receptor 1, Aurora kinase B, and Maternal Embryonic Leucine-zipper Kinase. Structure-based computational modeling allowed us to perform in silico docking of various small molecule candidates to these kinases. As a result, four novel small molecules demonstrated highly specific targeting of the key structural elements of these kinases. We then synthesized these identified candidate compounds and confirmed their in vitro inhibition of the targeting kinase activities. Finally, we will also present our data about the treatment effect with these compounds on the in vitro BTSC growth and the in vivo tumor growth. CONCLUSION. Structure-based drug design will likely open up a new avenue to discover novel anti-cancer stem cell agents that can selectively target the key kinases in BTSC, leading to the growth arrest of malignant glioma. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3298. doi:10.1158/1538-7445.AM2011-3298