Identification of Novel p38α MAP Kinase Inhibitors Using Fragment-Based Lead Generation

Novel Fragment Inhibitors of PYCR1 from Docking-Guided X-ray Crystallography

The proline biosynthetic enzyme Δ1-pyrroline-5-carboxylate (P5C) reductase 1 (PYCR1) is one of the most consistently upregulated enzymes across multiple cancer types and central to the metabolic rewiring of cancer cells. Herein, we describe a fragment-based, structure-first approach to the discovery of PYCR1 inhibitors. Thirty-seven fragment-like carboxylic acids in the molecular weight range of 143-289 Da were selected from docking and then screened using X-ray crystallography as the primary assay. Strong electron density was observed for eight compounds, corresponding to a crystallographic hit rate of 22%. The fragments are novel compared to existing proline analog inhibitors in that they block both the P5C substrate pocket and the NAD(P)H binding site. Four hits showed inhibition of PYCR1 in kinetic assays, and one has lower apparent IC50 than the current best proline analog inhibitor. These results show proof-of-concept for our inhibitor discovery approach and provide a basis for fragment-to-lead optimization.

Preventing lipophilic aggregation in cosolvent molecular dynamics simulations with hydrophobic probes using Plumed Automatic Restraining Tool (PART)

Cosolvent molecular dynamics (MD) simulations are molecular dynamics simulations used to identify preferable locations of small organic fragments on a protein target. Most cosolvent molecular dynamics workflows make use of only water-soluble fragments, as hydrophobic fragments would cause lipophilic aggregation. To date the two approaches that allow usage of hydrophobic cosolvent molecules are to use a low (0.2 M) concentration of hydrophobic probes, with the disadvantage of a lower sampling speed, or to use force field modifications, with the disadvantage of a difficult and inflexible setup procedure. Here we present a third alternative, that does not suffer from low sampling speed nor from cumbersome preparation procedures. We have built an easy-to-use open source command line tool PART (Plumed Automatic Restraining Tool) to generate a PLUMED file handling all intermolecular restraints to prevent lipophilic aggregation. We have compared restrained and unrestrained cosolvent MD simulations, showing that restraints are necessary to prevent lipophilic aggregation at hydrophobic probe concentrations of 0.5 M. Furthermore, we benchmarked PART generated restraints on a test set of four proteins (Factor-Xa, HIV protease, P38 MAP kinase and RNase A), showing that cosolvent MD with PART generated restraints qualitatively reproduces binding features of cocrystallised ligands.

Deciphering neuroprotective mechanism of nitroxoline in cerebral ischemia: network pharmacology and molecular modeling-based investigations

Cerebral ischemia is one of the major causes of death and disability worldwide. Currently, existing approved therapies are based on reperfusion and there is an unmet need to search for drugs with neuroprotective effects. The present study aims to investigate the neuroprotective mechanisms of nitroxoline, a nitro derivative of 8-Hydroxyquinoline, against cerebral ischemia using integrated network pharmacology and molecular docking approaches. Critical analytical tools used were SwissTarget, PharmMapper, BindingDB, DisGeNet, Cytoscape, GeneMANIA, ShinyGo, Metascape, GeneCodis, and Schrodinger GLIDE. Thirty-six overlapping drug and disease targets were identified and used for further analysis. Gene Ontology results showed that nitroxoline enriched the genes involved in biological processes of oxidative stress and apoptotic cell death that are highly implicated in hypoxic injury. KEGG enrichment analysis showed nitroxoline influenced a total of 159 biological pathways, out of which, top pathways involved in cerebral ischemia included longevity regulating pathway, VEGF signaling, EGFR tyrosine kinase inhibitor resistance, IL-17 and HIF-1 pathways, FoxO signaling, and AGE-RAGE pathway. Protein-protein interaction analysis using string database showed PARP1, EGFR, PTEN, BRD4, RAC1, NOS2, MTOR, MAPK3, BCL2, MAPK1, APP, METAP2, MAPK14, SIRT1, PRKAA1, and MCL1 as highly interactive proteins involved in pathogenesis of ischemic stroke regulated by nitroxoline. The highly interactive protein targets were validated by molecular docking studies and molecular dynamic simulations. Amongst all these targets, nitroxoline showed the highest binding affinity towards BRD4 followed by PARP1 and PTEN. Nitroxoline, through network pharmacology analysis, showed a role in regulating proteins, biological processes, and pathways crucial in cerebral ischemia. The current study thus provides a preliminary insight that nitroxoline might be used as a neuroprotectant against cerebral ischemia via modulating the epigenetic reader BRD4 and transcription factors such as RELA, NF-κβ1, and SP1. However, further in-vitro and preclinical studies need to be performed for concrete evidence.

The Hitchhiker's Guide to Deep Learning Driven Generative Chemistry

This microperspective covers the most recent research outcomes of artificial intelligence (AI) generated molecular structures from the point of view of the medicinal chemist. The main focus is on studies that include synthesis and experimental in vitro validation in biochemical assays of the generated molecular structures, where we analyze the reported structures' relevance in modern medicinal chemistry and their novelty. The authors believe that this review would be appreciated by medicinal chemistry and AI-driven drug design (AIDD) communities and can be adopted as a comprehensive approach for qualifying different research outcomes in AIDD.

Tackling Hysteresis in Conformational Sampling: How to Be Forgetful with MEMENTO

The structure of proteins has long been recognized to hold the key to understanding and engineering their function, and rapid advances in structural biology and protein structure prediction are now supplying researchers with an ever-increasing wealth of structural information. Most of the time, however, structures can only be determined in free energy minima, one at a time. While conformational flexibility may thus be inferred from static end-state structures, their interconversion mechanisms─a central ambition of structural biology─are often beyond the scope of direct experimentation. Given the dynamical nature of the processes in question, many studies have attempted to explore conformational transitions using molecular dynamics (MD). However, ensuring proper convergence and reversibility in the predicted transitions is extremely challenging. In particular, a commonly used technique to map out a path from a starting to a target conformation called steered MD (SMD) can suffer from starting-state dependence (hysteresis) when combined with techniques such as umbrella sampling (US) to compute the free energy profile of a transition. Here, we study this problem in detail on conformational changes of increasing complexity. We also present a new, history-independent approach that we term "MEMENTO" (Morphing End states by Modelling Ensembles with iNdependent TOpologies) to generate paths that alleviate hysteresis in the construction of conformational free energy profiles. MEMENTO utilizes template-based structure modelling to restore physically reasonable protein conformations based on coordinate interpolation (morphing) as an ensemble of plausible intermediates, from which a smooth path is picked. We compare SMD and MEMENTO on well-characterized test cases (the toy peptide deca-alanine and the enzyme adenylate kinase) before discussing its use in more complicated systems (the kinase P38α and the bacterial leucine transporter LeuT). Our work shows that for all but the simplest systems SMD paths should not in general be used to seed umbrella sampling or related techniques, unless the paths are validated by consistent results from biased runs in opposite directions. MEMENTO, on the other hand, performs well as a flexible tool to generate intermediate structures for umbrella sampling. We also demonstrate that extended end-state sampling combined with MEMENTO can aid the discovery of collective variables on a case-by-case basis.

p38α Kinase Auto-Activation through Its Conformational Transition Induced by Tyr323 Phosphorylation

p38α is a key serine/threonine kinase that can enable atypical auto-activation through Zap70 phosphorylation and initiate T cell receptor signaling. The auto-activation plays an important role in autoimmune diseases. Although the classical activation mechanism of p38α has been studied in-depth, the atypical activation mechanism of Y323 phosphorylation-induced p38α auto-activation remains largely unexplained, especially the regulatory effects of phosphorylation on different sites (Y323 vs T180). From the X-ray experimental data, we identified the inactive and active states of p38α using principal component analysis. To understand the auto-activation process and the internal driving mechanism, a computational paradigm that couples the targeted molecular dynamics simulations, the String Method, and the umbrella sampling strategy were employed to generate the conformational landscape of p38α, including p38α T180-Y323, p38α T180-pY323, and p38α pT180-pY323 systems (pT180/pY323: phosphorylated T180/Y323). We explored that pY323 could change the conformational distribution and promote the conformational transition of p38α from the inactive state to the active state. Auto-activation of p38α is regulated by pY323 through destabilization of the hydrophobic core structure and aided by R173. This study will further explain the conformational transition of p38α induced by Y323 phosphorylation and provide insights into the universal molecular auto-activation mechanism of the p38 subfamily at the atomic level.

Advances in computational methods for ligand binding kinetics

Binding kinetic parameters can be correlated with drug efficacy, which in recent years led to the development of various computational methods for predicting binding kinetic rates and gaining insight into protein-drug binding paths and mechanisms. In this review, we introduce and compare computational methods recently developed and applied to two systems, trypsin-benzamidine and kinase-inhibitor complexes. Methods involving enhanced sampling in molecular dynamics simulations or machine learning can be used not only to predict kinetic rates, but also to reveal factors modulating the duration of residence times, selectivity, and drug resistance to mutations. Methods which require less computational time to make predictions are highlighted, and suggestions to reduce the error of computed kinetic rates are presented.

Designing of kinase hinge binders: A medicinal chemistry perspective

Protein kinases are key regulators of cellular signaling and play a critical role in oncogenesis. Inhibitors of protein kinases are pursued by both industry and academia as a promising target for cancer therapy. Within the protein kinases, the ATP site has produced more than 40 FDA-approved drugs. The ATP site is broadly composed of a hinge region, gatekeeper residues, DFG-loop, ribose pocket, and other hydrophobic regions. The hinge region in the ATP site can be used for designing potent inhibitors. In this review, we discuss some representative studies that will highlight the interactions of heterocyclic compounds with hinge regions of different kinases like BRAF kinase, EGRF kinase, MAP kinase, and Mps1 kinase.

Accelerated Ligand-Mapping Molecular Dynamics Simulations for the Detection of Recalcitrant Cryptic Pockets and Occluded Binding Sites

The identification and characterization of binding sites is a critical component of structure-based drug design (SBDD). Probe-based/cosolvent molecular dynamics (MD) methods that allow for protein flexibility have been developed to predict ligand binding sites. However, cryptic pockets that appear only upon ligand binding and occluded binding sites with no access to the solvent pose significant challenges to these methods. Here, we report the development of accelerated ligand-mapping MD (aLMMD), which combines accelerated MD with LMMD, for the detection of these challenging binding sites. The method was validated on five proteins with what we term "recalcitrant" cryptic pockets, which are deeply buried pockets that require extensive movement of the protein backbone to expose, and three proteins with occluded binding sites. In all the cases, aLMMD was able to detect and sample the binding sites. Our results suggest that aLMMD could be used as a general approach for the detection of such elusive binding sites in protein targets, thus providing valuable information for SBDD.

A new strategy for hit generation: Novel in cellulo active inhibitors of CYP121A1 from Mycobacterium tuberculosis via a combined X-ray crystallographic and phenotypic screening approach (XP screen)

There is a pressing need for new drugs against tuberculosis (TB) to combat the growing resistance to current antituberculars. Herein a novel strategy is described for hit generation against promising TB targets involving X-ray crystallographic screening in combination with phenotypic screening. This combined approach (XP Screen) affords both a validation of target engagement as well as determination of in cellulo activity. The utility of this method is illustrated by way of an XP Screen against CYP121A1, a cytochrome P450 enzyme from Mycobacterium tuberculosis (Mtb) championed as a validated drug discovery target. A focused screening set was synthesized and tested by such means, with several members of the set showing promising activity against Mtb strain H37Rv. One compound was observed as an X-ray hit against CYP121A1 and showed improved activity against Mtb strain H37Rv under multiple assay conditions (pan-assay activity). Data obtained during X-ray crystallographic screening were utilized in a structure-based campaign to design a limited number of analogues (less than twenty), many of which also showed pan-assay activity against Mtb strain H37Rv. These included the benzo[b][1,4]oxazine derivative (MIC₉₀ 6.25 μM), a novel hit compound suitable as a starting point for a more involved hit to lead candidate medicinal chemistry campaign.

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CYP121 from M.tuberculosis has been previously shown to be a crucial target for the survival of the mycobacteria.

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Strategies previously employed have identified high affinity inhibitors however these have lacked activity on M.tuberculosis.

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The strategy reported here uses a combination of X-ray crystallography and phenotypic screening (XP Screen) to identify compounds.

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The XP screen approach identified a number of compounds which show good affinity (up to 3.2 μM) and MIC against M.tuberculosis (up to 6.25 μM).

Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins

Selectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a specific target, while shared areas can provide insights into polypharmacology. As the quantity of structural data grows, automated methods are needed to process, summarize, and present these data to users. We present a computational method that provides quantitative and data-driven summaries of the available binding site information from an ensemble of structures of the same protein. The resulting ensemble maps identify the key interactions important for ligand binding in the ensemble. The comparison of ensemble maps of related proteins enables the identification of selectivity-determining regions within a protein family. We applied the method to three examples from the well-researched human bromodomain and kinase families, demonstrating that the method is able to identify selectivity-determining regions that have been used to introduce selectivity in past drug discovery campaigns. We then illustrate how the resulting maps can be used to automate comparisons across a target protein family.

A network pharmacology analysis on drug-like compounds from Ganoderma lucidum for alleviation of atherosclerosis

Ganoderma lucidum (GL) is known as a potent alleviator against chronic inflammatory disease like atherosclerosis (AS), but its mechanisms against AS have not been unveiled. This research aimed to identify the key compounds(s) and mechanism(s) of GL against AS through network pharmacology. The compounds from GL were identified by gas chromatography-mass spectrum (GC-MS), and SwissADME screened their physicochemical properties. Then, the target(s) associated with the screened compound(s) or AS related targets were identified by public databases, and we selected the overlapping targets using a Venn diagram. The networks between overlapping targets and compounds were visualized, constructed, and analyzed by RStudio. Finally, we performed a molecular docking test (MDT) to explore key target(s), compound(s), on AutoDockVina. A total of 35 compounds in GL were detected via GC-MS, and 34 compounds (accepted by Lipinski's rule) were selected as drug-like compounds (DLCs). A total of 34 compounds were connected to the number of 785 targets, and DisGeNET and Online Mendelian Inheritance in Man (OMIM) identified 2,606 AS-related targets. The final 98 overlapping targets were extracted between the compounds-targets and AS-related targets. On Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, the number of 27 signaling pathways were sorted out, and a hub signaling pathway (MAPK signaling pathway), a core gene (PRKCA), and a key compound (Benzamide, 4-acetyl-N-[2,6-dimethylphenyl]) were selected among the 27 signaling pathways via MDT. Overall, we found that the identified 3 DLCs from GL have potent anti-inflammatory efficacy, improving AS by inactivating the MAPK signaling pathway. PRACTICAL APPLICATIONS: Ganoderma lucidum (GL) has been used as a medicinal or edible mushroom for chronic inflammatory patients: diabetes mellitus and dyslipidemia, especially atherosclerosis (AS). Until now, the majority of mushroom research has been implemented regarding β-glucan derivatives with very hydrophilic physicochemical properties. It implies that β-glucan or its derivatives have poor bioavailability. Hence, we have involved GC-MS in identifying lipophilic compounds from GL, which filtered them in silico to sort drug-like compounds (DLCs). Then, we retrieved targets associated with the DLCs, and identified a key signaling pathway, key targets, and key compounds against AS. In this paper, we utilized bioinformatics and network pharmacology theory to understand the uncovered pharmacological mechanism of GL on AS. To sum things up, our analysis elucidates the relationships between signaling pathways, targets, and compounds in GL. Ultimately, this work provides biochemical evidence to identify the therapeutic effect of GL on AS, and a scientific basis for deciphering the key mechanism on DLCs of GL against AS.

Bioinspired Photoredox Benzylation of Quinones

3-Benzylmenadiones were obtained in good yield by using a blue-light-induced photoredox process in the presence of Fe(III), oxygen, and γ-terpinene acting as a hydrogen-atom transfer agent. This methodology is compatible with a wide variety of diversely substituted 1,4-naphthoquinones as well as various cheap, readily available benzyl bromides with excellent functional group tolerance. The benzylation mechanism was investigated and supports a three-step radical cascade with the key involvement of the photogenerated superoxide anion radical.

Prediction of the Drug-Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis

There is growing consensus that the optimization of the kinetic parameters for drug-protein binding leads to improved drug efficacy. Therefore, computational methods have been developed to predict kinetic rates and to derive quantitative structure-kinetic relationships (QSKRs). Many of these methods are based on crystal structures of ligand-protein complexes. However, a drawback is that each ligand-protein complex is usually treated as having a single structure. Here, we present a modification of COMparative BINding Energy (COMBINE) analysis, which uses the structures of ligand-protein complexes to predict binding parameters. We introduce the option of using multiple structures to describe each ligand-protein complex in COMBINE analysis and apply this to study the effects of protein flexibility on the derivation of dissociation rate constants (k_off) for inhibitors of p38 mitogen-activated protein (MAP) kinase, which has a flexible binding site. Multiple structures were obtained for each ligand-protein complex by performing docking to an ensemble of protein configurations obtained from molecular dynamics simulations. Coefficients to scale ligand-protein interaction energies determined from energy-minimized structures of ligand-protein complexes were obtained by partial least squares regression, and they allowed for the computation of k_off values. The QSKR model obtained using single, energy-minimized crystal structures for each ligand-protein complex had higher predictive power than the QSKR model obtained with multiple structures from ensemble docking. However, incorporation of ligand-protein flexibility helped to highlight additional ligand-protein interactions that lead to longer residence times, such as interactions with residues Arg67 and Asp168, which are close to the ligand in many crystal structures. These results show that COMBINE analysis is a promising method to guide the design of compounds that bind to flexible proteins with improved binding kinetics.

Reaction of Diisobutylaluminum Borohydride, a Binary Hydride, with Selected Organic Compounds Containing Representative Functional Groups

The binary hydride, diisobutylaluminum borohydride [(iBu)₂AlBH₄], synthesized from diisobutylaluminum hydride (DIBAL) and borane dimethyl sulfide (BMS) has shown great potential in reducing a variety of organic functional groups. This unique binary hydride, (iBu)₂AlBH₄, is readily synthesized, versatile, and simple to use. Aldehydes, ketones, esters, and epoxides are reduced very fast to the corresponding alcohols in essentially quantitative yields. This binary hydride can reduce tertiary amides rapidly to the corresponding amines at 25 °C in an efficient manner. Furthermore, nitriles are converted into the corresponding amines in essentially quantitative yields. These reactions occur under ambient conditions and are completed in an hour or less. The reduction products are isolated through a simple acid-base extraction and without the use of column chromatography. Further investigation showed that (iBu)₂AlBH₄ has the potential to be a selective hydride donor as shown through a series of competitive reactions. Similarities and differences between (iBu)₂AlBH₄, DIBAL, and BMS are discussed.

Prediction of kinase inhibitors binding modes with machine learning and reduced descriptor sets

Protein kinases are receiving wide research interest, from drug perspective, due to their important roles in human body. Available kinase-inhibitor data, including crystallized structures, revealed many details about the mechanism of inhibition and binding modes. The understanding and analysis of these binding modes are expected to support the discovery of kinase-targeting drugs. The huge amounts of data made it possible to utilize computational techniques, including machine learning, to help in the discovery of kinase-targeting drugs. Machine learning gave reasonable predictions when applied to differentiate between the binding modes of kinase inhibitors, promoting a wider application in that domain. In this study, we applied machine learning supported by feature selection techniques to classify kinase inhibitors according to their binding modes. We represented inhibitors as a large number of molecular descriptors, as features, and systematically reduced these features in a multi-step manner while trying to attain high classification accuracy. Our predictive models could satisfy both goals by achieving high accuracy while utilizing at most 5% of the modeling features. The models could differentiate between binding mode types with MCC values between 0.67 and 0.92, and balanced accuracy values between 0.78 and 0.97 for independent test sets.

Dihydroartemisinin as a potential drug candidate for cancer therapy: a structural-based virtual screening for multitarget profiling

Cancer is a rapidly growing non-communicable disease worldwide that is responsible for high mortality rates, which account for 9.6 million death in 2018. Dihydroartemisinin (DHA) is an active metabolite of artemisinin, an active principle present in the Chinese medicinal plant Artemisia annua used for malaria treatment. Dihydroartemisinin possesses remarkable and selective anticancer properties however the underlying mechanism of the antitumor effects of DHA from the structural point of view is still not yet elucidated. In the present study, we employed molecular docking simulation techniques using Autodock suits to access the binding properties of dihydroartemisinin to multiple protein targets implicated in cancer pathogenesis. Its potential targets with comprehensive pharmacophore were predicted using a PharmMapper database. The co-crystallised structures of the protein were obtained from a Protein Data Bank and prepared for molecular docking simulation. Out of the 24 selected protein targets, DHA has shown about 29% excellent binding to the targets compared to their co-crystallised ligand. Additionally, 75% of the targets identified for dihydroartemisinin binding are protein kinases, and 25% are non-protein kinases. Hydroxyl functional group of dihydroartemisinin contributed to 58.5% of the total hydrogen interactions, while pyran (12.2%), endoperoxide (9.8%), and oxepane (19.5%) contributed to the remaining hydrogen bonding. The present findings have elucidated the possible antitumor properties of dihydroartemisinin through the structural-based virtual studies, which provides a lead to a safe and effective anticancer agent useful for cancer therapy.Communicated by Ramaswamy H. Sarma.

A Novel Small-Molecule Inhibitor of Endosomal TLRs Reduces Inflammation and Alleviates Autoimmune Disease Symptoms in Murine Models

Toll-like receptors (TLRs) play a fundamental role in the inflammatory response against invading pathogens. However, the dysregulation of TLR-signaling pathways is implicated in several autoimmune/inflammatory diseases. Here, we show that a novel small molecule TLR-inhibitor (TAC5) and its derivatives TAC5-a, TAC5-c, TAC5-d, and TAC5-e predominantly antagonized poly(I:C) (TLR3)-, imiquimod (TLR7)-, TL8-506 (TLR8)-, and CpG-oligodeoxynucleotide (TLR9)-induced signaling pathways. TAC5 and TAC5-a significantly hindered the activation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), reduced the phosphorylation of mitogen-activated protein kinases, and inhibited the secretion of tumor necrosis factor-α (TNF-α) and interleukin-6. Besides, TAC5-a prevented the progression of psoriasis and systemic lupus erythematosus (SLE) in mice. Interestingly, TAC5 and TAC5-a did not affect Pam₃CSK₄ (TLR1/2)-, FSL-1 (TLR2/6)-, or lipopolysaccharide (TLR4)-induced TNF-α secretion, indicating their specificity towards endosomal TLRs (TLR3/7/8/9). Collectively, our data suggest that the TAC5 series of compounds are potential candidates for treating autoimmune diseases such as psoriasis or SLE.

Transient States and Barriers from Molecular Simulations and the Milestoning Theory: Kinetics in Ligand-Protein Recognition and Compound Design

This study presents a novel computational approach to study molecular recognition and binding kinetics for drug-like compounds dissociating from a flexible protein system. The intermediates and their free energy profile during ligand association and dissociation processes control ligand-protein binding kinetics and bring a more complete picture of ligand-protein binding. The method applied the milestoning theory to extract kinetics and thermodynamics information from running short classical molecular dynamics (MD) simulations for frames from a given dissociation path. High-dimensional ligand-protein motions (3N-6 degrees of freedom) during ligand dissociation were reduced by use of principal component modes for assigning more than 100 milestones, and classical MD runs were allowed to travel multiple milestones to efficiently obtain ensemble distribution of initial structures for MD simulations and estimate the transition time and rate during ligand traveling between milestones. We used five pyrazolourea ligands and cyclin-dependent kinase 8 with cyclin C (CDK8/CycC) as our model system as well as metadynamics and a pathway search method to sample dissociation pathways. With our strategy, we constructed the free energy profile for highly mobile biomolecular systems. The computed binding free energy and residence time correctly ranked the pyrazolourea ligand series, in agreement with experimental data. Guided by a barrier of a ligand passing an αC helix and activation loop, we introduced one hydroxyl group to parent compounds to design our ligands with increased residence time and validated our prediction by experiments. This work provides a novel and robust approach to investigate dissociation kinetics of large and flexible systems for understanding unbinding mechanisms and designing new small-molecule drugs with desired binding kinetics.

Large-Scale Biomolecular Conformational Transitions Explored by a Combined Elastic Network Model and Enhanced Sampling Molecular Dynamics

Biomolecules often undergo large-scale conformational transitions when carrying out their functions. However, it is still challenging for conventional molecular dynamics simulations to provide adequate structural dynamics information to interpret associated mechanisms. Here, we present a combined elastic network model and enhanced sampling-based strategy (iterANM-IaMD) by adopting iterANM to construct initial conformation space and enhanced sampling IaMD to explore the free energy landscape along specific large-scale conformational transitions. We applied this strategy to three functionally and structurally distinct proteins (adenylate kinase, calmodulin, and p38α kinase), which undergo striking conformational change upon ligand binding. The simulation results for both free and ligand-bound proteins show qualitative and quantitative agreement with existing studies, suggesting iterANM-IaMD as an accurate and efficient tool to investigate structural dynamics involved in complicated biological processes. Our work also provides insights into the relationship between the dynamics and functionality of biomolecules.

A Selective and Brain Penetrant p38αMAPK Inhibitor Candidate for Neurologic and Neuropsychiatric Disorders That Attenuates Neuroinflammation and Cognitive Dysfunction

The p38αMAPK is a serine/threonine protein kinase and a key node in the intracellular signaling networks that transduce and amplify stress signals into physiological changes. A preponderance of preclinical data and clinical observations established p38αMAPK as a brain drug discovery target involved in neuroinflammatory responses and synaptic dysfunction in multiple degenerative and neuropsychiatric brain disorders. We summarize the discovery of highly selective, brain-penetrant, small molecule p38αMAPK inhibitors that are efficacious in diverse animal models of neurologic disorders. A crystallography and pharmacoinformatic approach to fragment expansion enabled the discovery of an efficacious hit. The addition of secondary pharmacology screens to refinement delivered lead compounds with improved selectivity, appropriate pharmacodynamics, and efficacy. Safety considerations and additional secondary pharmacology screens drove optimization that delivered the drug candidate MW01-18-150SRM (MW150), currently in early stage clinical trials.

Emerging and Re-Emerging Warheads for Targeted Covalent Inhibitors: Applications in Medicinal Chemistry and Chemical Biology

Targeted covalent inhibitors (TCIs) are designed to bind poorly conserved amino acids by means of reactive groups, the so-called warheads. Currently, targeting noncatalytic cysteine residues with acrylamides and other α,β-unsaturated carbonyl compounds is the predominant strategy in TCI development. The recent ascent of covalent drugs has stimulated considerable efforts to characterize alternative warheads for the covalent-reversible and irreversible engagement of noncatalytic cysteine residues as well as other amino acids. This Perspective article provides an overview of warheads-beyond α,β-unsaturated amides-recently used in the design of targeted covalent ligands. Promising reactive groups that have not yet demonstrated their utility in TCI development are also highlighted. Special emphasis is placed on the discussion of reactivity and of case studies illustrating applications in medicinal chemistry and chemical biology.

Medicinal chemistry of vicinal diaryl scaffold: A mini review

The privileged structures have been widely used as a valuable template in new drug discovery. 1,2-Diaryl or vicinal diaryl is a simple scaffold found in many drugs and naturally occurring compounds. From synthetic point of view, the vicinal diaryl derivatives are easily accessible due to their facile and expedient syntheses. These scaffolds have shown numerous interesting pharmacological activities against various diseases with lot of clinical potentials. This review aims to highlight the evidence of vicinal diaryl motif as a privileged scaffold in COX-2 inhibitors and CA-4 analogs.

1,2,4-Trisubstituted imidazolinones with dual carbonic anhydrase and p38 mitogen-activated protein kinase inhibitory activity

Various 1,2,4 trisubstituted imidazolin-5-one derivatives were synthesized and evaluated for their inhibitory activity against p38 mitogen-activated protein kinase (p38MAPK) and carbonic anhydrase (CA) enzymes aiming to explore potential dual inhibitors. Results revealed that compounds 3c, 3g, 3h, 4a, 6c and 6d were the most effective derivatives against p38αMAPK (IC₅₀ = 0.14, 0.14, 0.056, 0.14, 0.13 and 0.14 μM, respectively) compared to sorafenib (IC₅₀ = 1.58 μM) as standard drug. On the other hand, compound 4a revealed the best inhibitory activity against all the tested carbonic anhydrase isoforms CA I, II, IV and IX with K_i values of 95.0, 0.83, 6.90 and 12.4 nM, respectively compared to acetazolamide with K_i values 250, 12.1, 74 and 12.8 nM, respectively. Therefore, compound 4a can be considered as a potent dual p38αMAPK/CA inhibitor.

Role of Molecular Interactions and Protein Rearrangement in the Dissociation Kinetics of p38α MAP Kinase Type-I/II/III Inhibitors

Understanding the governing factors of fast or slow inhibitor binding/unbinding assists in developing drugs with preferred kinetic properties. For inhibitors with the same binding affinity targeting different binding sites of the same protein, the kinetic behavior can profoundly differ. In this study, we investigated unbinding kinetics and mechanisms of fast (type-I) and slow (type-II/III) binders of p38α mitogen-activated protein kinase, where the crystal structures showed that type-I and type-II/III inhibitors bind to pockets with different conformations of the Asp-Phe-Gly (DFG) motif. The work used methods that combine conventional molecular dynamics (MD), accelerated molecular dynamics (AMD) simulations, and the newly developed pathway search guided by internal motions (PSIM) method to find dissociation pathways. The study focuses on revealing key interactions and molecular rearrangements that hinder ligand dissociation by using umbrella sampling and post-MD processing to examine changes in free energy during ligand unbinding. As anticipated, the initial dissociation steps all require breaking interactions that appeared in crystal structures of the bound complexes. Interestingly, for type-I inhibitors such as SB2, p38α keeps barrier-free conformational fluctuation in the ligand-bound complex and during ligand dissociation. In contrast, with a type-II/III inhibitor such as BIRB796, with the rearrangements of p38α in its bound state, ligand unbinding features energetically unfavorable protein-ligand concerted movement. Our results also show that the type-II/III inhibitors preferred dissociation pathways through the allosteric channel, which is consistent with an existing publication. The study suggests that the level of required protein rearrangement is one major determining factor of drug binding kinetics in p38α systems, providing useful information for development of inhibitors.

NMR-Fragment Based Virtual Screening: A Brief Overview

Fragment-based drug discovery (FBDD) using NMR has become a central approach over the last twenty years for development of small molecule inhibitors against biological macromolecules, to control a variety of cellular processes. Yet, several considerations should be taken into account for obtaining a therapeutically relevant agent. In this review, we aim to list the considerations that make NMR fragment screening a successful process for yielding potent inhibitors. Factors that may govern the competence of NMR in fragment based drug discovery are discussed, as well as later steps that involve optimization of hits obtained by NMR-FBDD.

A multistep docking and scoring protocol for congeneric series: Implementation on kinase DFG-out type II inhibitors

AIM

Rescoring of docking-binding poses can significantly improve molecular docking results. Our aim was to evaluate postprocessing docking protocols in order to determine the most suitable methodology for the study of the binding of congeneric compounds to protein kinases.

MATERIALS & METHODS

Diverse ligand-receptor poses generated after docking were submitted to different relaxation protocols. The Molecular Mechanics Poisson-Boltzmann (Generalized Born) Surface Area approach was applied for the evaluation of the binding affinity of complexes obtained. The performance of various Molecular Mechanics Poisson-Boltzmann (Generalized Born) Surface Area methodologies was compared.

RESULTS

The inclusion of a postprocessing protocol after docking enhances the quality of the results, although the best methodology is system dependent.

CONCLUSION

An examination of the interactions established has allowed us to suggest useful modifications for the design of new type II inhibitors.

A Comprehensive Structural Overview of p38α Mitogen-Activated Protein Kinase in Complex with ATP-Site and Non-ATP-Site Binders

Herein we review all the currently available ATP-site and non-ATP-site ligands bound to p38α mitogen-activated protein kinase (MAPK) available in the RCSB Protein Data Bank (PDB). The co-crystallized inhibitors have been classified into different families according to their experimental binding mode and chemical structure, and the ligand-protein interactions are discussed using the most representative compounds. This systematic structural analysis could provide some take-home lessons for drug discovery programs aimed at the rational identification and optimization of new p38α MAPK inhibitors.

Discovery of naphthyl-N-acylhydrazone p38α MAPK inhibitors with in vivo anti-inflammatory and anti-TNF-α activity

Protein kinases constitute attractive therapeutic targets for development of new prototypes to treat different chronic diseases. Several available drugs, like tinibs, are tyrosine kinase inhibitors; meanwhile, inhibitors of serine/threonine kinases, such as mitogen-activated protein kinase (MAPK), are still trying to overcome some problems in one of the steps of clinical development to become drugs. So, here we reported the synthesis, the in vitro kinase inhibitory profile, docking studies, and the evaluation of anti-inflammatory profile of new naphthyl-N-acylhydrazone derivatives using animal models. Although all tested compounds (3a-d) have been characterized as p38α MAPK inhibitors and have showed in vivo anti-inflammatory action, LASSBio-1824 (3b) presented the best performance as p38α MAPK inhibitor, with IC₅₀  = 4.45 μm, and also demonstrated to be the most promising anti-inflammatory prototype, with good in vivo anti-TNF-α profile after oral administration.

Optimized 4,5-Diarylimidazoles as Potent/Selective Inhibitors of Protein Kinase CK1δ and Their Structural Relation to p38α MAPK

The involvement of protein kinase CK1δ in the pathogenesis of severe disorders such as Alzheimer’s disease, amyotrophic lateral sclerosis, familial advanced sleep phase syndrome, and cancer has dramatically increased interest in the development of effective small molecule inhibitors for both therapeutic application and basic research. Unfortunately, the design of CK1 isoform-specific compounds has proved to be highly complicated due to the existence of six evolutionarily conserved human CK1 members that possess similar, different, or even opposite physiological and pathophysiological implications. Consequently, only few potent and selective CK1δ inhibitors have been reported so far and structurally divergent approaches are urgently needed in order to establish SAR that might enable complete discrimination of CK1 isoforms and related p38α MAPK. In this study we report on design and characterization of optimized 4,5-diarylimidazoles as highly effective ATP-competitive inhibitors of CK1δ with compounds 11b (IC₅₀ CK1δ = 4 nM, IC₅₀ CK1ε = 25 nM), 12a (IC₅₀ CK1δ = 19 nM, IC₅₀ CK1ε = 227 nM), and 16b (IC₅₀ CK1δ = 8 nM, IC₅₀ CK1ε = 81 nM) being among the most potent CK1δ-targeting agents published to date. Inhibitor compound 11b, displaying potential as a pharmacological tool, has further been profiled over a panel of 321 protein kinases exhibiting high selectivity. Cellular efficacy has been evaluated in human pancreatic cancer cell lines Colo357 (EC₅₀ = 3.5 µM) and Panc89 (EC₅₀ = 1.5 µM). SAR is substantiated by X-ray crystallographic analysis of 16b in CK1δ and 11b in p38α.

Fragment-Based Discovery of Potent and Selective DDR1/2 Inhibitors

The DDR1 and DDR2 receptor tyrosine kinases are activated by extracellular collagen and have been implicated in a number of human diseases including cancer. We performed a fragment-based screen against DDR1 and identified fragments that bound either at the hinge or in the back pocket associated with the DFG-out conformation of the kinase. Modeling based on crystal structures of potent kinase inhibitors facilitated the "back-to-front" design of potent DDR1/2 inhibitors that incorporated one of the DFG-out fragments. Further optimization led to low nanomolar, orally bioavailable inhibitors that were selective for DDR1 and DDR2. The inhibitors were shown to potently inhibit DDR2 activity in cells but in contrast to unselective inhibitors such as dasatinib, they did not inhibit proliferation of mutant DDR2 lung SCC cell lines.

A Comprehensive Structural Overview of p38α MAPK in Complex with Type I Inhibitors

p38α mitogen-activated protein kinase (MAPK) is a well-recognized therapeutic target for the treatment of autoimmune and inflammatory diseases. Over the past two decades, tremendous efforts have been focused on the discovery and development of small-molecule p38α MAPK inhibitors, although currently no drugs targeting this protein are clinically available. Therefore, the identification of novel chemotypes that are able to inhibit p38α MAPK function is still of great therapeutic significance. With the objective to support drug discovery programs aimed at identifying new immunomodulators acting on p38α MAPK, herein we present a complete overview of the available crystal structures of this protein in complex with ATP-site type I inhibitors. The 85 available complexes are classified by chemotype and experimental binding mode, and the ligand-protein interactions are discussed using the most representative inhibitors. The type and frequency of key inhibitor features are analyzed to give a final summary of the chemical requirements of promising p38α MAPK inhibitors. The proposed pharmacophore can be exploited to enhance the opportunities to identify novel type I inhibitors of p38α MAPK.

Novel approaches for targeting kinases: allosteric inhibition, allosteric activation and pseudokinases

Protein kinases are involved in many essential cellular processes and their deregulation can lead to a variety of diseases, including cancer. The pharmaceutical industry has invested heavily in the identification of kinase inhibitors to modulate these disease-promoting pathways, resulting in several successful drugs. However, the field is challenging as it is difficult to identify novel selective inhibitors with good pharmacokinetic/pharmacodynamic properties. In addition, resistance to kinase inhibitor treatment frequently arises. The identification of non-ATP site targeting ('allosteric') inhibitors, the identification of kinase activators and the expansion of kinase target space to include the less studied members of the family, including atypical- and pseudo-kinases, are potential avenues to overcome these challenges. In this perspective, the opportunities and challenges of following these approaches and others will be discussed.

Kinase crystal identification and ATP-competitive inhibitor screening using the fluorescent ligand SKF86002

The small kinase inhibitor SKF86002 lacks intrinsic fluorescence but becomes fluorescent upon binding to the ATP-binding sites of p38 mitogen-activated protein kinase (p38α). It was found that co-crystals of this compound with various kinases were distinguishable by their strong fluorescence. The co-crystals of SKF86002 with p38α, Pim1, ASK1, HCK and AMPK were fluorescent. Addition of SKF86002, which binds to the ATP site, to the co-crystallization solution of HCK promoted protein stability and thus facilitated the production of crystals that otherwise would not grow in the apo form. It was further demonstrated that the fluorescence of SKF86002 co-crystals can be applied to screen for candidate kinase inhibitors. When a compound binds competitively to the ATP-binding site of a kinase crystallized with SKF86002, it displaces the fluorescent SKF86002 and the crystal loses its fluorescence. Lower fluorescent signals were reported after soaking SKF86002-Pim1 and SKF86002-HCK co-crystals with the inhibitors quercetin, a quinazoline derivative and A-419259. Determination of the SKF86002-Pim1 and SKF86002-HCK co-crystal structures confirmed that SKF86002 interacts with the ATP-binding sites of Pim1 and HCK. The structures of Pim1-SKF86002 crystals soaked with the inhibitors quercetin and a quinazoline derivative and of HCK-SKF86002 crystals soaked with A-419259 were determined. These structures were virtually identical to the deposited crystal structures of the same complexes. A KINOMEscan assay revealed that SKF86002 binds a wide variety of kinases. Thus, for a broad range of kinases, SKF86002 is useful as a crystal marker, a crystal stabilizer and a marker to identify ligand co-crystals for structural analysis.

Exploring transition pathway and free-energy profile of large-scale protein conformational change by combining normal mode analysis and umbrella sampling molecular dynamics

Large-scale conformational changes of proteins are usually associated with the binding of ligands. Because the conformational changes are often related to the biological functions of proteins, understanding the molecular mechanisms of these motions and the effects of ligand binding becomes very necessary. In the present study, we use the combination of normal-mode analysis and umbrella sampling molecular dynamics simulation to delineate the atomically detailed conformational transition pathways and the associated free-energy landscapes for three well-known protein systems, viz., adenylate kinase (AdK), calmodulin (CaM), and p38α kinase in the absence and presence of respective ligands. For each protein under study, the transient conformations along the conformational transition pathway and thermodynamic observables are in agreement with experimentally and computationally determined ones. The calculated free-energy profiles reveal that AdK and CaM are intrinsically flexible in structures without obvious energy barrier, and their ligand binding shifts the equilibrium from the ligand-free to ligand-bound conformation (population shift mechanism). In contrast, the ligand binding to p38α leads to a large change in free-energy barrier (ΔΔG ≈ 7 kcal/mol), promoting the transition from DFG-in to DFG-out conformation (induced fit mechanism). Moreover, the effect of the protonation of D168 on the conformational change of p38α is also studied, which reduces the free-energy difference between the two functional states of p38α and thus further facilitates the conformational interconversion. Therefore, the present study suggests that the detailed mechanism of ligand binding and the associated conformational transition is not uniform for all kinds of proteins but correlated to their respective biological functions.

Inclusion of multiple fragment types in the site identification by ligand competitive saturation (SILCS) approach

The site identification by ligand competitive saturation (SILCS) method identifies the location and approximate affinities of small molecular fragments on a target macromolecular surface by performing molecular dynamics (MD) simulations of the target in an aqueous solution of small molecules representative of different chemical functional groups. In this study, we introduce a set of small molecules to map potential interactions made by neutral hydrogen bond donors and acceptors and charged donor and acceptor fragments in addition to nonpolar fragments. The affinity pattern is obtained in the form of discretized probability or, equivalently, free energy maps, called FragMaps, which can be visualized with the target surface. We performed SILCS simulations for four proteins for which structural and thermodynamic data is available for multiple diverse ligands. Good overlap is shown between high affinity regions identified by the FragMaps and the crystallographic positions of ligand functional groups with similar chemical functionality, thus demonstrating the validity of the qualitative information obtained from the simulations. To test the ability of FragMaps in providing quantitative predictions, we calculate the previously introduced ligand grid free energy (LGFE) metric and observe its correspondence with experimentally measured binding affinity. LGFE is computed for different conformational ensembles and improvement in prediction is shown with increasing ligand conformational sampling. Ensemble generation includes a Monte Carlo sampling approach that uses the GFE FragMaps directly as the energy function. The results show that some but not all experimental trends are predicted and warrant improvements in the scoring methodology. In addition, the potential utility of atom-based free energy contributions to the LGFE scores and the use of multiple ligands in SILCS to identify displaceable water molecules during ligand design are discussed.

Rapid identification of ligand-binding sites by using an assignment-free NMR approach

In this study, we developed an assignment-free approach for rapid identification of ligand-binding sites in target proteins by using NMR. With a sophisticated cell-free stable isotope-labeling procedure that introduces (15)N- or (13)C-labels to specific atoms of target proteins, this approach requires only a single series of ligand titrations with labeled targets. Using titration data, ligand-binding sites in the target protein can be identified without time-consuming assignment procedures. We demonstrated the feasibility of this approach by using structurally well-characterized interactions between mitogen-activated protein (MAP) kinase p38α and its inhibitor 2-amino-3-benzyloxypyridine. Furthermore, we confirmed the recently proposed fatty acid binding to p38α and confirmed the fatty acid-binding site in the MAP kinase insert region.

KLIFS: a knowledge-based structural database to navigate kinase-ligand interaction space

Protein kinases regulate the majority of signal transduction pathways in cells and have become important targets for the development of designer drugs. We present a systematic analysis of kinase-ligand interactions in all regions of the catalytic cleft of all 1252 human kinase-ligand cocrystal structures present in the Protein Data Bank (PDB). The kinase-ligand interaction fingerprints and structure database (KLIFS) contains a consistent alignment of 85 kinase ligand binding site residues that enables the identification of family specific interaction features and classification of ligands according to their binding modes. We illustrate how systematic mining of kinase-ligand interaction space gives new insights into how conserved and selective kinase interaction hot spots can accommodate the large diversity of chemical scaffolds in kinase ligands. These analyses lead to an improved understanding of the structural requirements of kinase binding that will be useful in ligand discovery and design studies.

Prediction of the binding affinity of compounds with diverse scaffolds by MP-CAFEE

Accurate methods to predict the binding affinities of compounds for target molecules are powerful tools in structure-based drug design (SBDD). A recently developed method called massively parallel computation of absolute binding free energy with a well-equilibrated system (MP-CAFEE) successfully predicted the binding affinities of compounds with relatively similar scaffolds. We investigate the applicability of MP-CAFEE for predicting the affinity of compounds having more diverse scaffolds for the target p38α, a mitogen-activated protein kinase. The calculated and experimental binding affinities correlate well, showing that MP-CAFEE can accurately rank the compounds with diverse scaffolds. We propose a method to determine the optimal number of sampling runs with respect to a predefined level of accuracy, which is established according to the stage in the SBDD process being considered. The optimal number of sampling runs for two key stages-lead identification and lead optimization-is estimated to be five and eight or more, respectively, in our model system using Cochrans sample size formula.

Contributions of water transfer energy to protein-ligand association and dissociation barriers: Watermap analysis of a series of p38α MAP kinase inhibitors

In our previous work, we proposed that desolvation and resolvation of the binding sites of proteins can serve as the slowest steps during ligand association and dissociation, respectively, and tested this hypothesis on two protein-ligand systems with known binding kinetics behavior. In the present work, we test this hypothesis on another kinetically-determined protein-ligand system-that of p38α and eight Type II BIRB 796 inhibitor analogs. The kon values among the inhibitor analogs are narrowly distributed (10⁴ ≤ kon ≤ 10⁵ M⁻¹ s⁻¹), suggesting a common rate-determining step, whereas the koff values are widely distributed (10⁻¹ ≤ koff ≤ 10⁻⁶ s⁻¹), suggesting a spectrum of rate-determining steps. We calculated the solvation properties of the DFG-out protein conformation using an explicit solvent molecular dynamics simulation and thermodynamic analysis method implemented in WaterMap to predict the enthalpic and entropic costs of water transfer to and from bulk solvent incurred upon association and dissociation of each inhibitor. The results suggest that the rate-determining step for association consists of the transfer of a common set of enthalpically favorable solvating water molecules from the binding site to bulk solvent. The rate-determining step for inhibitor dissociation consists of the transfer of water from bulk solvent to specific binding site positions that are unfavorably solvated in the apo protein, and evacuated during ligand association. Different sets of unfavorable solvation are evacuated by each ligand, and the observed dissociation barriers are qualitatively consistent with the calculated solvation free energies of those sets.

Molecular approaches towards development of purified natural products and their structurally known derivatives as efficient anti-cancer drugs: current trends

Several natural products and their derivatives, either in purified or structurally identified form, exhibit immense pharmacological and biological properties, some of them showing considerable anticancer potential. Although the molecular mechanisms of action of some of these products are yet to be elucidated, extensive research in this area continues to generate new data that are clinically exploitable. Recent advancement in molecular biology, high throughput screening, biomarker identifications, target selection and genomic approaches have enabled us to understand salient interactions of natural products and their derivatives with cancer cells vis-à-vis normal cells. In this review we highlight the recent approaches and application of innovative technologies made to improve quality as well as efficiency of structurally identified natural products and their derivatives, particularly in small molecular forms capable of being used in "targeted therapies" in oncology. These products preferentially involve multiple mechanistic pathways and overcome chemo-resistance in tumor types with cumulative action. We also mention briefly a few physico-chemical features that compare natural products with drugs in recent natural product discovery approaches. We further report here a few purified natural products as examples that provide molecular interventions in cancer therapeutics to give the reader a glimpse of the current trends of approach for discovering useful anticancer drugs.