Accurate ligand-protein docking in CASP15 using the ClusPro LigTBM server.

In the ligand prediction category of CASP15, the challenge was to predict the positions and conformations of small molecules binding to proteins that were provided as amino acid sequences or as models generated by the AlphaFold2 program. For most targets, we used our template-based ligand docking program ClusPro ligTBM, also implemented as a public server available at https://ligtbm.cluspro.org/. Since many targets had multiple chains and a number of ligands, several templates, and some manual interventions were required. In a few cases, no templates were found, and we had to use direct docking using the Glide program. Nevertheless, ligTBM was shown to be a very useful tool, and by any ranking criteria, our group was ranked among the top five best-performing teams. In fact, all the best groups used template-based docking methods. Thus, it appears that the AlphaFold2-generated models, despite the high accuracy of the predicted backbone, have local differences from the x-ray structure that make the use of direct docking methods more challenging. The results of CASP15 confirm that this limitation can be frequently overcome by homology-based docking.

The therapeutic relevance of the Kallikrein-Kinin axis in SARS-cov-2-induced vascular pathology.

While coronavirus disease 2019 (COVID-19) begins as a respiratory infection, it progresses as a systemic disease involving multiorgan microthromboses that underly the pathology. SARS-CoV-2 enters host cells via attachment to the angiotensin-converting enzyme 2 (ACE2) receptor. ACE2 is widely expressed in a multitude of tissues, including the lung (alveolar cells), heart, intestine, kidney, testis, gallbladder, vasculature (endothelial cells), and immune cells. Interference in ACE2 signaling could drive the aforementioned systemic pathologies, such as endothelial dysfunction, microthromboses, and systemic inflammation, that are typically seen in patients with severe COVID-19. ACE2 is a component of the renin-angiotensin system (RAS) and is intimately associated with the plasma kallikrein-kinin system (KKS). As many papers are published on the role of ACE and ACE2 in COVID-19, we will review the role of bradykinin, and more broadly the KSS, in SARS-CoV-2-induced vascular dysfunction. Furthermore, we will discuss the possible therapeutic interventions that are approved and in development for the following targets: coagulation factor XII (FXII), tissue kallikrein (KLK1), plasma kallikrein (KLKB1), bradykinin (BK), plasminogen activator inhibitor (PAI-1), bradykinin B1 receptor (BKB1R), bradykinin B2 receptor (BKB2R), ACE, furin, and the NLRP3 inflammasome. Understanding these targets may prove of value in the treatment of COVID-19 as well as in other virus-induced coagulopathies in the future.

High Accuracy Prediction of PROTAC Complex Structures.

The design of PROteolysis-TArgeting Chimeras (PROTACs) requires bringing an E3 ligase into proximity with a target protein to modulate the concentration of the latter through its ubiquitination and degradation. Here, we present a method for generating high-accuracy structural models of E3 ligase-PROTAC-target protein ternary complexes. The method is dependent on two computational innovations: adding a "silent" convolution term to an efficient protein-protein docking program to eliminate protein poses that do not have acceptable linker conformations and clustering models of multiple PROTACs that use the same E3 ligase and target the same protein. Results show that the largest consensus clusters always have high predictive accuracy and that the ensemble of models can be used to predict the dissociation rate and cooperativity of the ternary complex that relate to the degrading activity of the PROTAC. The method is demonstrated by applications to known PROTAC structures and a blind test involving PROTACs against BRAF mutant V600E. The results confirm that PROTACs function by stabilizing a favorable interaction between the E3 ligase and the target protein but do not necessarily exploit the most energetically favorable geometry for interaction between the proteins.

Functional characterization of a PROTAC directed against BRAF mutant V600E.

The RAF family kinases function in the RAS-ERK pathway to transmit signals from activated RAS to the downstream kinases MEK and ERK. This pathway regulates cell proliferation, differentiation and survival, enabling mutations in RAS and RAF to act as potent drivers of human cancers. Drugs targeting the prevalent oncogenic mutant BRAF(V600E) have shown great efficacy in the clinic, but long-term effectiveness is limited by resistance mechanisms that often exploit the dimerization-dependent process by which RAF kinases are activated. Here, we investigated a proteolysis-targeting chimera (PROTAC) approach to BRAF inhibition. The most effective PROTAC, termed P4B, displayed superior specificity and inhibitory properties relative to non-PROTAC controls in BRAF(V600E) cell lines. In addition, P4B displayed utility in cell lines harboring alternative BRAF mutations that impart resistance to conventional BRAF inhibitors. This work provides a proof of concept for a substitute to conventional chemical inhibition to therapeutically constrain oncogenic BRAF.

A drug discovery platform to identify compounds that inhibit EGFR triple mutants.

Receptor tyrosine kinases (RTKs) are transmembrane receptors of great clinical interest due to their role in disease. Historically, therapeutics targeting RTKs have been identified using in vitro kinase assays. Due to frequent development of drug resistance, however, there is a need to identify more diverse compounds that inhibit mutated but not wild-type RTKs. Here, we describe MaMTH-DS (mammalian membrane two-hybrid drug screening), a live-cell platform for high-throughput identification of small molecules targeting functional protein-protein interactions of RTKs. We applied MaMTH-DS to an oncogenic epidermal growth factor receptor (EGFR) mutant resistant to the latest generation of clinically approved tyrosine kinase inhibitors (TKIs). We identified four mutant-specific compounds, including two that would not have been detected by conventional in vitro kinase assays. One of these targets mutant EGFR via a new mechanism of action, distinct from classical TKI inhibition. Our results demonstrate how MaMTH-DS is a powerful complement to traditional drug screening approaches.

Discovery of an MLLT1/3 YEATS Domain Chemical Probe.

YEATS domain (YD) containing proteins are an emerging class of epigenetic targets in drug discovery. Dysregulation of these modified lysine-binding proteins has been linked to the onset and progression of cancers. We herein report the discovery and characterisation of the first small-molecule chemical probe, SGC-iMLLT, for the YD of MLLT1 (ENL/YEATS1) and MLLT3 (AF9/YEATS3). SGC-iMLLT is a potent and selective inhibitor of MLLT1/3-histone interactions. Excellent selectivity over other human YD proteins (YEATS2/4) and bromodomains was observed. Furthermore, our probe displays cellular target engagement of MLLT1 and MLLT3. The first small-molecule X-ray co-crystal structures with the MLLT1 YD are also reported. This first-in-class probe molecule can be used to understand MLLT1/3-associated biology and the therapeutic potential of small-molecule YD inhibitors.

Discovery of OICR12694: A Novel, Potent, Selective, and Orally Bioavailable BCL6 BTB Inhibitor.

B cell lymphoma 6 (BCL6), a highly regulated transcriptional repressor, is deregulated in several forms of non-Hodgkin lymphoma (NHL), most notably in diffuse large B-cell lymphoma (DLBCL). The activities of BCL6 are dependent on protein-protein interactions with transcriptional co-repressors. To find new therapeutic interventions addressing the needs of patients with DLBCL, we initiated a program to identify BCL6 inhibitors that interfere with co-repressor binding. A virtual screen hit with binding activity in the high micromolar range was optimized by structure-guided methods, resulting in a novel and highly potent inhibitor series. Further optimization resulted in the lead candidate 58 (OICR12694/JNJ-65234637), a BCL6 inhibitor with low nanomolar DLBCL cell growth inhibition and an excellent oral pharmacokinetic profile. Based on its overall favorable preclinical profile, OICR12694 is a highly potent, orally bioavailable candidate for testing BCL6 inhibition in DLBCL and other neoplasms, particularly in combination with other therapies.

Discovery of Nanomolar DCAF1 Small Molecule Ligands.

DCAF1 is a substrate receptor of two distinct E3 ligases (CRL4DCAF1 and EDVP), plays a critical physiological role in protein degradation, and is considered a drug target for various cancers. Antagonists of DCAF1 could be used toward the development of therapeutics for cancers and viral treatments. We used the WDR domain of DCAF1 to screen a 114-billion-compound DNA encoded library (DEL) and identified candidate compounds using similarity search and machine learning. This led to the discovery of a compound (Z1391232269) with an SPR KD of 11 µM. Structure-guided hit optimization led to the discovery of OICR-8268 (26e) with an SPR KD of 38 nM and cellular target engagement with EC50 of 10 µM as measured by cellular thermal shift assay (CETSA). OICR-8268 is an excellent tool compound to enable the development of next-generation DCAF1 ligands toward cancer therapeutics, further investigation of DCAF1 functions in cells, and the development of DCAF1-based PROTACs.

The omega-3 hydroxy fatty acid 7(S)-HDHA is a high-affinity PPARα ligand that regulates brain neuronal morphology.

The nuclear receptor peroxisome proliferator-activated receptor alpha (PPARα) is emerging as an important target in the brain for the treatment or prevention of cognitive disorders. The identification of high-affinity ligands for brain PPARα may reveal the mechanisms underlying the synaptic effects of this receptor and facilitate drug development. Here, using an affinity purification-untargeted mass spectrometry (AP-UMS) approach, we identified an endogenous, selective PPARα ligand, 7(S)-hydroxy-docosahexaenoic acid [7(S)-HDHA]. Results from mass spectrometric detection of 7(S)-HDHA in mouse and rat brain tissues, time-resolved FRET analyses, and thermal shift assays collectively revealed that 7(S)-HDHA potently activated PPARα with an affinity greater than that of other ligands identified to date. We also found that 7(S)-HDHA activation of PPARα in cultured mouse cortical neurons stimulated neuronal growth and arborization, as well as the expression of genes associated with synaptic plasticity. The findings suggest that this DHA derivative supports and enhances neuronal synaptic capacity in the brain.

Phenolic Lipids Derived from Cashew Nut Shell Liquid to Treat Metabolic Diseases.

Metabolic diseases are increasing at staggering rates globally. The peroxisome proliferator-activated receptors (PPARα/γ/δ) are fatty acid sensors that help mitigate imbalances between energy uptake and utilization. Herein, we report compounds derived from phenolic lipids present in cashew nut shell liquid (CNSL), an abundant waste byproduct, in an effort to create effective, accessible, and sustainable drugs. Derivatives of anacardic acid and cardanol were tested for PPAR activity in HEK293 cell co-transfection assays, primary hepatocytes, and 3T3-L1 adipocytes. In vivo studies using PPAR-expressing zebrafish embryos identified CNSL derivatives with varying tissue-specific activities. LDT409 (23) is an analogue of cardanol with partial agonist activity for PPARα and PPARγ. Pharmacokinetic profiling showed that 23 is orally bioavailable with a half-life of 4 h in mice. CNSL derivatives represent a sustainable source of selective PPAR modulators with balanced intermediate affinities (EC50 ∼ 100 nM to 10 µM) that provide distinct and favorable gene activation profiles for the treatment of diabetes and obesity.

Design, Synthesis, and Characterization of 4-Aminoquinazolines as Potent Inhibitors of the G Protein-Coupled Receptor Kinase 6 (GRK6) for the Treatment of Multiple Myeloma.

Both previous and additional genetic knockdown studies reported herein implicate G protein-coupled receptor kinase 6 (GRK6) as a critical kinase required for the survival of multiple myeloma (MM) cells. Therefore, we sought to develop a small molecule GRK6 inhibitor as an MM therapeutic. From a focused library of known kinase inhibitors, we identified two hits with moderate biochemical potencies against GRK6. From these hits, we developed potent (IC50 < 10 nM) analogues with selectivity against off-target kinases. Further optimization led to the discovery of an analogue (18) with an IC50 value of 6 nM against GRK6 and selectivity against a panel of 85 kinases. Compound 18 has potent cellular target engagement and antiproliferative activity against MM cells and is synergistic with bortezomib. In summary, we demonstrate that targeting GRK6 with small molecule inhibitors represents a promising approach for MM and identify 18 as a novel, potent, and selective GRK6 inhibitor.

Chemical Genetics Screen Identifies COPB2 Tool Compounds That Alters ER Stress Response and Induces RTK Dysregulation in Lung Cancer Cells.

Activating mutations in the epidermal growth factor receptor (EGFR) are common driver mutations in non-small cell lung cancer (NSCLC). First, second and third generation EGFR tyrosine kinase inhibitors (TKIs) are effective at inhibiting mutant EGFR NSCLC, however, acquired resistance is a major issue, leading to disease relapse. Here, we characterize a small molecule, EMI66, an analog of a small molecule which we previously identified to inhibit mutant EGFR signalling via a novel mechanism of action. We show that EMI66 attenuates receptor tyrosine kinase (RTK) expression and signalling and alters the electrophoretic mobility of Coatomer Protein Complex Beta 2 (COPB2) protein in mutant EGFR NSCLC cells. Moreover, we demonstrate that EMI66 can alter the subcellular localization of EGFR and COPB2 within the early secretory pathway. Furthermore, we find that COPB2 knockdown reduces the growth of mutant EGFR lung cancer cells, alters the post-translational processing of RTKs, and alters the endoplasmic reticulum (ER) stress response pathway. Lastly, we show that EMI66 treatment also alters the ER stress response pathway and inhibits the growth of mutant EGFR lung cancer cells and organoids. Our results demonstrate that targeting of COPB2 with EMI66 presents a viable approach to attenuate mutant EGFR signalling and growth in NSCLC.

Hybrid Design of Isonicotinic Acid Hydrazide Derivatives: Machine Learning Studies, Synthesis and Biological Evaluation of their Antituberculosis Activity.

BACKGROUND: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis (Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs. OBJECTIVE: The goal of this work is to identify potent small molecule anti-TB agents by machine learning, synthesis and biological evaluation. METHODS: The On-line Chemical Database and Modeling Environment (OCHEM) was used to build predictive machine learning models. Seven compounds were synthesized and tested in vitro for their antitubercular activity against H37Rv and resistant Mtb strains. RESULTS: A set of predictive models was built with OCHEM based on a set of previously synthesized isoniazid (INH) derivatives containing a thiazole core and tested against Mtb. The predictive ability of the models was tested by a 5-fold cross-validation, and resulted in balanced accuracies (BA) of 61-78% for the binary classifiers. Test set validation showed that the models could be instrumental in predicting anti- TB activity with a reasonable accuracy (with BA = 67-79 %) within the applicability domain. Seven designed compounds were synthesized and demonstrated activity against both the H37Rv and multidrugresistant (MDR) Mtb strains resistant to rifampicin and isoniazid. According to the acute toxicity evaluation in Daphnia magna neonates, six compounds were classified as moderately toxic (LD50 in the range of 10-100 mg/L) and one as practically harmless (LD50 in the range of 100-1000 mg/L). CONCLUSION: The newly identified compounds may represent a starting point for further development of therapies against Mtb. The developed models are available online at OCHEM http://ochem.eu/article/11 1066 and can be used to virtually screen for potential compounds with anti-TB activity.

Leveraging an Open Science Drug Discovery Model to Develop CNS-Penetrant ALK2 Inhibitors for the Treatment of Diffuse Intrinsic Pontine Glioma.

There are currently no effective chemotherapeutic drugs approved for the treatment of diffuse intrinsic pontine glioma (DIPG), an aggressive pediatric cancer resident in the pons region of the brainstem. Radiation therapy is beneficial but not curative, with the condition being uniformly fatal. Analysis of the genomic landscape surrounding DIPG has revealed that activin receptor-like kinase-2 (ALK2) constitutes a potential target for therapeutic intervention given its dysregulation in the disease. We adopted an open science approach to develop a series of potent, selective, orally bioavailable, and brain-penetrant ALK2 inhibitors based on the lead compound LDN-214117. Modest structural changes to the C-3, C-4, and C-5 position substituents of the core pyridine ring afforded compounds M4K2009, M4K2117, and M4K2163, each with a superior potency, selectivity, and/or blood-brain barrier (BBB) penetration profile. Robust in vivo pharmacokinetic (PK) properties and tolerability mark these inhibitors as advanced preclinical compounds suitable for further development and evaluation in orthotopic models of DIPG.

Discovery of a Selective Inhibitor for the YEATS Domains of ENL/AF9.

Eleven-nineteen leukemia (ENL) contains an epigenetic reader domain (YEATS domain) that recognizes lysine acylation on histone 3 and facilitates transcription initiation and elongation through its interactions with the super elongation complex (SEC) and the histone methyl transferase DOT1L. Although it has been known for its role as a fusion protein in mixed lineage leukemia (MLL), overexpression of native ENL, and thus dysregulation of downstream genes in acute myeloid leukemia (AML), has recently been implicated as a driver of disease that is reliant on the epigenetic reader activity of the YEATS domain. We developed a peptide displacement assay (histone 3 tail with acylated lysine) and screened a small-molecule library totaling more than 24,000 compounds for their propensity to disrupt the YEATS domain-histone peptide binding. Among these, we identified a first-in-class dual inhibitor of ENL ( Kd = 745 ± 45 nM) and its paralog AF9 ( Kd = 523 ± 53 nM) and performed "SAR by catalog" with the aim of starting the development of a chemical probe for ENL.

De Novo Design of Boron-Based Peptidomimetics as Potent Inhibitors of Human ClpP in the Presence of Human ClpX.

Boronic acids have attracted the attention of synthetic and medicinal chemists due to boron's ability to modulate enzyme function. Recently, we demonstrated that boron-containing amphoteric building blocks facilitate the discovery of bioactive aminoboronic acids. Herein, we have augmented this capability with a de novo library design and a virtual screening platform modified for covalent ligands. This technique has allowed us to rapidly design and identify a series of α-aminoboronic acids as the first inhibitors of human ClpXP, which is responsible for the degradation of misfolded proteins.

In silico ligand-based modeling of hBACE-1 inhibitors.

Alzheimer's disease is a chronic neurodegenerative disease affecting more than 30 million people worldwide. Development of small molecule inhibitors of human ß-secretase 1 (hBACE-1) is being the focus of pharmaceutical industry for the past 15-20 years. Here, we successfully applied multiple ligand-based in silico modeling techniques to understand the inhibitory activities of a diverse set of small molecule hBACE-1 inhibitors reported in the scientific literature. Strikingly, the use of only a small subset of 230 (13%) molecules allowed us to develop quality models that performed reasonably well on the validation set of 1,476 (87%) inhibitors. Varying the descriptor sets and the complexity of the modeling techniques resulted in only minor improvements to the model's performance. The current results demonstrate that predictive models can be built by choosing appropriate modeling techniques in spite of using small datasets consisting of diverse chemical classes, a scenario typical in triaging of high-throughput screening results to identify false negatives. We hope that these encouraging results will help the community to develop more predictive models that would support research efforts for the debilitating Alzheimer's disease. Additionally, the integrated diversity of the techniques employed will stimulate scientists in the field to use in silico statistical modeling techniques like these to derive better models to help advance the drug discovery projects faster.

Rational design of isonicotinic acid hydrazide derivatives with antitubercular activity: Machine learning, molecular docking, synthesis and biological testing.

The problem of designing new antitubercular drugs against multiple drug-resistant tuberculosis (MDR-TB) was addressed using advanced machine learning methods. As there are only few published measurements against MDR-TB, we collected a large literature data set and developed models against the non-resistant H37Rv strain. The predictive accuracy of these models had a coefficient of determination q2  = .7-.8 (regression models) and balanced accuracies of about 80% (classification models) with cross-validation and independent test sets. The models were applied to screen a virtual chemical library, which was designed to have MDR-TB activity. The seven most promising compounds were identified, synthesized and tested. All of them showed activity against the H37Rv strain, and three molecules demonstrated activity against the MDR-TB strain. The docking analysis indicated that the discovered molecules could bind enoyl reductase, InhA, which is required in mycobacterial cell wall development. The models are freely available online (http://ochem.eu/article/103868) and can be used to predict potential anti-TB activity of new chemicals.

A New, Improved Hybrid Scoring Function for Molecular Docking and Scoring Based on AutoDock and AutoDock Vina.

Automated docking is one of the most important tools for structure-based drug design that allows prediction of ligand binding poses and also provides an estimate of how well small molecules fit in the binding site of a protein. A new scoring function based on AutoDock and AutoDock Vina has been introduced. The new hybrid scoring function is a linear combination of the two scoring function components derived from a multiple linear regression fitting procedure. The scoring function was built on a training set of 2412 protein-ligand complexes from pdbbind database (www.pdbbind.org.cn, version 2012). A test set of 313 complexes that appeared in the 2013 version was used for validation purposes. The new hybrid scoring function performed better than the original functions, both on training and test sets of protein-ligand complexes, as measured by the non-parametric Pearson correlation coefficient, R, mean absolute error (MAE), and root-mean-square error (RMSE) between the experimental binding affinities and the docking scores. The function also gave one of the best results among more than 20 scoring functions tested on the core set of the pdbbind database. The new AutoDock hybrid scoring function will be implemented in modified version of AutoDock.