Rational approaches to improving selectivity in drug design

Molecular basis for differential recognition of an allosteric inhibitor by receptor tyrosine kinases

Understanding kinase-inhibitor selectivity continues to be a major objective in kinase drug discovery. We probe the molecular basis of selectivity of an allosteric inhibitor (MSC1609119A-1) of the insulin-like growth factor-I receptor kinase (IGF1RK), which has been shown to be ineffective for the homologous insulin receptor kinase (IRK). Specifically, we investigated the structural and energetic basis of the allosteric binding of this inhibitor to each kinase by combining molecular modeling, molecular dynamics (MD) simulations, and thermodynamic calculations. We predict the inhibitor conformation in the binding pocket of IRK and highlight that the charged residues in the histidine-arginine-aspartic acid (HRD) and aspartic acid-phenylalanine-glycine (DFG) motifs and the nonpolar residues in the binding pocket govern inhibitor interactions in the allosteric pocket of each kinase. We suggest that the conformational changes in the IGF1RK residues M1054 and M1079, movement of the ⍺C-helix, and the conformational stabilization of the DFG motif favor the selectivity of the inhibitor toward IGF1RK. Our thermodynamic calculations reveal that the observed selectivity can be rationalized through differences observed in the electrostatic interaction energy of the inhibitor in each inhibitor/kinase complex and the hydrogen bonding interactions of the inhibitor with the residue V1063 in IGF1RK that are not attained with the corresponding residue V1060 in IRK. Overall, our study provides a rationale for the molecular basis of recognition of this allosteric inhibitor by IGF1RK and IRK, which is potentially useful in developing novel inhibitors with improved affinity and selectivity.

Comprehensive analysis of resorcinyl-imidazole Hsp90 inhibitor design

Human Hsp90 chaperones are implicated in various aspects of cancer. Due to this, Hsp90 has been explored as potential target in cancer treatment. Initial attempts to use Hsp90 inhibitors in drug trials failed due to toxicity and inefficacy. The next generation of drugs were less toxic but still insufficiently effective in a clinical setting. Recently, a lot of effort is being put into understanding the consequences of Hsp90 isoform selective inhibition, expecting that this might hold the key in targeting Hsp90 for disease treatment. Here we investigate a series of compounds containing the aryl-resorcinol scaffold with a 5-membered ring as a promising class of new human Hsp90 inhibitors, reaching nanomolar affinity. We compare how the replacement of 5-membered ring, from thiadiazole to imidazole, as well as a variety of their substituents, influences the potency of these inhibitors for Hsp90 alpha and beta isoforms. To further elucidate the dissimilarity in ligand selectivity between the isoforms, a mutant protein was constructed and tested against the ligand library. In addition, we performed a series of molecular dynamics (MD) and docking simulations to further explain our experimental findings as well as evaluated key compounds in cell assays. Our results deepen the understanding of Hsp90 isoform ligand selectivity and serve as an informative base for further Hsp90 inhibitor optimization.

Structure-Based Drug Design of ADRA2A Antagonists Derived from Yohimbine

Yohimbine, a natural indole alkaloid and a nonselective adrenoceptor antagonist, possesses potential benefits in treating inflammatory disorders and sepsis. Nevertheless, its broader clinical use faces challenges due to its low receptor selectivity. A structure-activity relationship study of novel yohimbine analogues identified amino esters of yohimbic acid as potent and selective ADRA2A antagonists. Specifically, amino ester 4n, in comparison to yohimbine, showed a 6-fold higher ADRA1A/ADRA2A selectivity index (SI > 556 for 4n) and a 25-fold higher ADRA2B/ADRA2A selectivity index. Compound 4n also demonstrated high plasma and microsomal stability, moderate-to-low membrane permeability determining its limited ability to cross the blood-brain barrier, and negligible toxicity on nontumor normal human dermal fibroblasts. Compound 4n represents an important complementary pharmacological tool to study the involvement of adrenoceptor subtypes in pathophysiologic conditions such as inflammation and sepsis and a novel candidate for further preclinical development to treat ADRA2A-mediated pathologies.

S. C, Gangadhar S, A. S, Murali M, Shivamallu C, Achar RR, Silina E, Stupin V, Manturova N, Shati AA, Alfaifi MY, Elbehairi SEI, Kollur SP, Amruthesh KN. Exploration of Type III effector Xanthomonas outer protein Q (XopQ) inhibitor from Picrasma quassioides as an antibacterial agent using chemoinformatics analysis

The present study was focused on exploring the efficient inhibitors of closed state (form) of type III effector Xanthomonas outer protein Q (XopQ) (PDB: 4P5F) from the 44 phytochemicals of Picrasma quassioides using cutting-edge computational analysis. Among them, Kumudine B showed excellent binding energy (-11.0 kcal/mol), followed by Picrasamide A, Quassidine I and Quassidine J with the targeted closed state of XopQ protein compared to the reference standard drug (Streptomycin). The molecular dynamics (MD) simulations performed at 300 ns validated the stability of top lead ligands (Kumudine B, Picrasamide A, and Quassidine I)-bound XopQ protein complex with slightly lower fluctuation than Streptomycin. The MM-PBSA calculation confirmed the strong interactions of top lead ligands (Kumudine B and QuassidineI) with XopQ protein, as they offered the least binding energy. The results of absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis confirmed that Quassidine I, Kumudine B and Picrasamide A were found to qualify most of the drug-likeness rules with excellent bioavailability scores compared to Streptomycin. Results of the computational studies suggested that Kumudine B, Picrasamide A, and Quassidine I could be considered potential compounds to design novel antibacterial drugs against X. oryzae infection. Further in vitro and in vivo antibacterial activities of Kumudine B, Picrasamide A, and Quassidine I are required to confirm their therapeutic potentiality in controlling the X. oryzae infection.

Prediction of the binding mechanism of a selective DNA methyltransferase 3A inhibitor by molecular simulation

DNA methylation is an epigenetic mechanism that introduces a methyl group at the C5 position of cytosine. This reaction is catalyzed by DNA methyltransferases (DNMTs) and is essential for the regulation of gene transcription. The DNMT1 and DNMT3A or -3B family proteins are known targets for the inhibition of DNA hypermethylation in cancer cells. A selective non-nucleoside DNMT3A inhibitor was developed that mimics S-adenosyl-l-methionine and deoxycytidine; however, the mechanism of selectivity is unclear because the inhibitor-protein complex structure determination is absent. Therefore, we performed docking and molecular dynamics simulations to predict the structure of the complex formed by the association between DNMT3A and the selective inhibitor. Our simulations, binding free energy decomposition analysis, structural isoform comparison, and residue scanning showed that Arg688 of DNMT3A is involved in the interaction with this inhibitor, as evidenced by its significant contribution to the binding free energy. The presence of Asn1192 at the corresponding residues in DNMT1 results in a loss of affinity for the inhibitor, suggesting that the interactions mediated by Arg688 in DNMT3A are essential for selectivity. Our findings can be applied in the design of DNMT-selective inhibitors and methylation-specific drug optimization procedures.

The binding of diruthenium (II,III) and dirhodium (II,II) paddlewheel complexes at DNA/RNA nucleobases: Computational evidences of an appreciable selectivity toward the AU base pairs

Multiple medicinal strategies involve modifications of the structure of DNA or RNA, which disrupt their correct functioning. Metal complexes with medicinal effects, also known as metallodrugs, are among the agents intended specifically for the attack onto nucleosides. The diruthenium (II,III) and dirhodium (II,II) paddlewheel complexes constitute promising dual acting drugs due to their ability to release the therapeutically active bridging ligands upon their substitution by endogenous ligands. In this paper, we study the structure and the stability of the complexes formed by the diruthenium (II,III) and dirhodium (II,II) paddlewheel complexes coordinated in axial positions with the DNA/RNA nucleobases or base pairs, assuming the attainable metalation at all the accessible pyridyl nitrogens. Dirhodium complexes coordinate at the pyridyl nitrogens more strongly than the diruthenium complexes. On the other hand, we found that the diruthenium scaffold binds more selectively to nucleobase targets. Furthermore, we reveal a tighter coordination of diruthenium complex at the adenine-uracil base pair, compared to adenine-thymine, hence constituting a scarce instance of RNA-selectivity. We envision that the here reported computational outcomes may pace future experiments addressing the binding of diruthenium and dirhodium paddlewheel complexes at either single nucleobases or DNA/RNA fragments.

Protein target similarity is positive predictor of in vitro antipathogenic activity: a drug repurposing strategy for Plasmodium falciparum

Drug discovery is an intricate and costly process. Repurposing existing drugs and active compounds offers a viable pathway to develop new therapies for various diseases. By leveraging publicly available biomedical information, it is possible to predict compounds' activity and identify their potential targets across diverse organisms. In this study, we aimed to assess the antiplasmodial activity of compounds from the Repurposing, Focused Rescue, and Accelerated Medchem (ReFRAME) library using in vitro and bioinformatics approaches. We assessed the in vitro antiplasmodial activity of the compounds using blood-stage and liver-stage drug susceptibility assays. We used protein sequences of known targets of the ReFRAME compounds with high antiplasmodial activity (EC50 < 10 uM) to conduct a protein-pairwise search to identify similar Plasmodium falciparum 3D7 proteins (from PlasmoDB) using NCBI protein BLAST. We further assessed the association between the compounds' in vitro antiplasmodial activity and level of similarity between their known and predicted P. falciparum target proteins using simple linear regression analyses. BLAST analyses revealed 735 P. falciparum proteins that were similar to the 226 known protein targets associated with the ReFRAME compounds. Antiplasmodial activity of the compounds was positively associated with the degree of similarity between the compounds' known targets and predicted P. falciparum protein targets (percentage identity, E value, and bit score), the number of the predicted P. falciparum targets, and their respective mutagenesis index and fitness scores (R2 between 0.066 and 0.92, P < 0.05). Compounds predicted to target essential P. falciparum proteins or those with a druggability index of 1 showed the highest antiplasmodial activity.

Structural Models for a Series of Allosteric Inhibitors of IGF1R Kinase

The allosteric inhibition of insulin-like growth factor receptor 1 kinase (IGF1RK) is a potential strategy to overcome selectivity barriers for targeting receptor tyrosine kinases. We constructed structural models of a series of 12 indole-butyl-amine derivatives that have been reported as allosteric inhibitors of IGF1RK. We further studied the dynamics and interactions of each inhibitor in the allosteric pocket via all-atom explicit-solvent molecular dynamics (MD) simulations. We discovered that a bulky carbonyl substitution at the R1 indole ring is structurally unfavorable for inhibitor binding in the IGF1RK allosteric pocket. Moreover, we found that the most potent derivative (termed C11) acquires a distinct conformation: forming an allosteric pocket channel with better shape complementarity and interactions with the receptor. In addition to a hydrogen-bonding interaction with V1063, the cyano derivative C11 forms a stable hydrogen bond with M1156, which is responsible for its unique binding conformation in the allosteric pocket. Our findings show that the positioning of chemical substituents with different pharmacophore features at the R1 indole ring influences molecular interactions and binding conformations of indole-butyl-amine derivatives and, hence, dramatically affects their potencies. Our results provide a structural framework for the design of allosteric inhibitors with improved affinities and specificities against IGF1RK.

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist's toolbox to enhance the efficiency of both hit optimization and candidate design. Both computational methods come with their own set of limitations, and they are often used independently of each other. ML's capability to screen extensive compound libraries expediently is tempered by its reliance on quality data, which can be scarce especially during early-stage optimization. Contrarily, physics-based approaches like free energy perturbation (FEP) are frequently constrained by low throughput and high cost by comparison; however, physics-based methods are capable of making highly accurate binding affinity predictions. In this study, we harnessed the strength of FEP to overcome data paucity in ML by generating virtual activity data sets which then inform the training of algorithms. Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. Throughout the paper, we emphasize key mechanistic considerations that must be taken into account when aiming to augment data sets and lay the groundwork for successful implementation. Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. We believe that the physics-based augmentation of ML will significantly benefit drug discovery, as these techniques continue to evolve.

Structural Models for a Series of Allosteric Inhibitors of IGF1R Kinase

The allosteric inhibition of Insulin-like Growth Factor Receptor 1 Kinase (IGF1RK) is a potential strategy to overcome selectivity barriers in targeting receptor tyrosine kinases. We constructed structural models of a series of 12 indole-butyl-amine derivatives which have been reported as allosteric inhibitors of IGF1RK. We further studied dynamics and interactions of each inhibitor in the allosteric pocket via all-atom explicit-solvent molecular dynamics (MD) simulations. We discovered that a bulky carbonyl substitution at the R1 indole ring is structurally unfavorable for inhibitor binding in the IGF1RK allosteric pocket. Moreover, we found that the most potent derivative (termed C11) acquires a distinct conformation, forming an allosteric pocket channel with better shape complementarity and interactions with the receptor. In addition to a hydrogen bonding interaction with V1063, the cyano derivative C11 forms a stable hydrogen bond with M1156, which is responsible for its unique binding conformation in the allosteric pocket. Our findings show that the position of chemical substituents at the R1 indole ring with different pharmacophore features influences molecular interactions and binding conformations of the indole-butyl-amine derivatives, hence dramatically affecting their potencies. Our results provide a structural framework for the design of allosteric inhibitors with improved affinities and specificities against IGF1RK.

Experimental structure based drug design (SBDD) applications for anti-leishmanial drugs: A paradigm shift?

Leishmaniasis is a group of neglected tropical diseases caused by at least 20 species of Leishmania protozoa, which are spread by the bite of infected sandflies. There are three main forms of the disease: cutaneous leishmaniasis (CL, the most common), visceral leishmaniasis (VL, also known as kala-azar, the most serious), and mucocutaneous leishmaniasis. One billion people live in areas endemic to leishmaniasis, with an annual estimation of 30,000 new cases of VL and more than 1 million of CL. New treatments for leishmaniasis are an urgent need, as the existing ones are inefficient, toxic, and/or expensive. We have revised the experimental structure-based drug design (SBDD) efforts applied to the discovery of new drugs against leishmaniasis. We have grouped the explored targets according to the metabolic pathways they belong to, and the key achieved advances are highlighted and evaluated. In most cases, SBDD studies follow high-throughput screening campaigns and are secondary to pharmacokinetic optimization, due to the majoritarian belief that there are few validated targets for SBDD in leishmaniasis. However, some SBDD strategies have significantly contributed to new drug candidates against leishmaniasis and a bigger number holds promise for future development.

Reinvent Aliphatic Arsenicals as Reversible Covalent Warheads toward Targeted Kinase Inhibition and Non-acute Promyelocytic Leukemia Cancer Treatment

The success of arsenic in acute promyelocytic leukemia (APL) treatment is hardly transferred to non-APL cancers, mainly due to the low selectivity and weak binding affinity of traditional arsenicals to oncoproteins critical for cancer survival. We present herein the reinvention of aliphatic trivalent arsenicals (As) as reversible covalent warheads of As-based targeting inhibitors toward Bruton's tyrosine kinase (BTK). The effects of As warheads' valency, thiol protection, methylation, spacer length, and size on inhibitors' activity were studied. We found that, in contrast to the bulky and rigid aromatic As warhead, the flexible aliphatic As warheads were well compatible with the well-optimized guiding group to achieve nanomolar inhibition against BTK. The optimized As inhibitors effectively blocked the BTK-mediated oncogenic signaling pathway, leading to elevated antiproliferative activities toward lymphoma cells and xenograft tumor. Our study provides a promising strategy enabling rational design of new aliphatic arsenic-based reversible covalent inhibitors toward non-APL cancer treatment.

Zeolitic imidazolate framework-8: a versatile nanoplatform for tissue regeneration

Extensive research on zeolitic imidazolate framework (ZIF-8) and its derivatives has highlighted their unique properties in nanomedicine. ZIF-8 exhibits advantages such as pH-responsive dissolution, easy surface functionalization, and efficient drug loading, making it an ideal nanosystem for intelligent drug delivery and phototherapy. These characteristics have sparked significant interest in its potential applications in tissue regeneration, particularly in bone, skin, and nerve regeneration. This review provides a comprehensive assessment of ZIF-8's feasibility in tissue engineering, encompassing material synthesis, performance testing, and the development of multifunctional nanosystems. Furthermore, the latest advancements in the field, as well as potential limitations and future prospects, are discussed. Overall, this review emphasizes the latest developments in ZIF-8 in tissue engineering and highlights the potential of its multifunctional nanoplatforms for effective complex tissue repair.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Discovery of novel, potent, selective and orally bioavailable HPK1 inhibitor for enhancing the efficacy of anti-PD-L1 antibody

Hematopoietic progenitor kinase 1 (HPK1), a serine/threonine kinase in the MAP4K family, is expressed predominantly in immune cells, and has been identified as a negative regulator of immune signaling. Accumulating evidences demonstrated that loss of HPK1 kinase function effectively enhances anti-tumor responses. In this study, we disclose the medicinal chemistry campaigns to discovery potent, selective, and orally active HPK1 inhibitors, starting from our previous work based on rigidification strategy. Systematically structure-activity relationship (SAR) exploration led to the identification of F03 (HMC-B17). The representative compound, HMC-B17, showed the potent HPK1 inhibition with an IC50 value of 1.39 nM and favorable selectivity against TCR-related kinases. In addition, the HMC-B17 effectively enhanced the IL-2 secretion in Jurkat cells (EC50 = 11.56 nM). Strikingly, immune-reverse effects and improved immune response in vivo were observed after HMC-B17 treatment. Furthermore, HMC-B17 combined with anti-PD-L1 antibody demonstrated a synergistic antitumor efficacy with TGI% value of 71.24 % in CT26 model. Collectively, our findings suggest that HMC-B17 could be a valuable lead compound to develop a safe and potent HPK1 inhibitor for further cancer immunotherapy.

Regulating ethical experimentation: Impacts of the breakthrough therapy designation on drug R&D

This article investigates patterns of pharmaceutical development activity around the 2012 creation of the FDA's Breakthrough Therapy Designation (BTD). The BTD introduced regulatory flexibility and support to avoid ethical challenges created by experimental therapies of exceptional performance in early stage clinical trials. We argue that the program's design indirectly created substantial incentives for the industry to pursue the designation. Consistent with this hypothesis, our evidence links the creation of the program with a substantial increase in the number of new drug indications entering the clinical trial process. This surge in introductions has resulted in a discernible increase in approvals, which has manifested with a lag and may strengthen in the future. Countering theoretical predictions, BTD incentives have not led to increased risk taking in project selection.

AI for targeted polypharmacology: The next frontier in drug discovery

In drug discovery, targeted polypharmacology, i.e., targeting multiple molecular targets with a single drug, is redefining therapeutic design to address complex diseases. Pre-selected pharmacological profiles, as exemplified in kinase drugs, promise enhanced efficacy and reduced toxicity. Historically, many of such drugs were discovered serendipitously, limiting predictability and efficacy, but currently artificial intelligence (AI) offers a transformative solution. Machine learning and deep learning techniques enable modeling protein structures, generating novel compounds, and decoding their polypharmacological effects, opening an avenue for more systematic and predictive multi-target drug design. This review explores the use of AI in identifying synergistic co-targets and delineating them from anti-targets that lead to adverse effects, and then discusses advances in AI-enabled docking, generative chemistry, and proteochemometric modeling of proteome-wide compound interactions, in the context of polypharmacology. We also provide insights into challenges ahead.

The Multifaceted Role of Cofilin in Neurodegeneration and Stroke: Insights into Pathogenesis and Targeting as a Therapy

This comprehensive review explores the complex role of cofilin, an actin-binding protein, across various neurodegenerative diseases (Alzheimer's, Parkinson's, schizophrenia, amyotrophic lateral sclerosis (ALS), Huntington's) and stroke. Cofilin is an essential protein in cytoskeletal dynamics, and any dysregulation could lead to potentially serious complications. Cofilin's involvement is underscored by its impact on pathological hallmarks like Aβ plaques and α-synuclein aggregates, triggering synaptic dysfunction, dendritic spine loss, and impaired neuronal plasticity, leading to cognitive decline. In Parkinson's disease, cofilin collaborates with α-synuclein, exacerbating neurotoxicity and impairing mitochondrial and axonal function. ALS and frontotemporal dementia showcase cofilin's association with genetic factors like C9ORF72, affecting actin dynamics and contributing to neurotoxicity. Huntington's disease brings cofilin into focus by impairing microglial migration and influencing synaptic plasticity through AMPA receptor regulation. Alzheimer's, Parkinson's, and schizophrenia exhibit 14-3-3 proteins in cofilin dysregulation as a shared pathological mechanism. In the case of stroke, cofilin takes center stage, mediating neurotoxicity and neuronal cell death. Notably, there is a potential overlap in the pathologies and involvement of cofilin in various diseases. In this context, referencing cofilin dysfunction could provide valuable insights into the common pathologies associated with the aforementioned conditions. Moreover, this review explores promising therapeutic interventions, including cofilin inhibitors and gene therapy, demonstrating efficacy in preclinical models. Challenges in inhibitor development, brain delivery, tissue/cell specificity, and long-term safety are acknowledged, emphasizing the need for precision drug therapy. The call to action involves collaborative research, biomarker identification, and advancing translational efforts. Cofilin emerges as a pivotal player, offering potential as a therapeutic target. However, unraveling its complexities requires concerted multidisciplinary efforts for nuanced and effective interventions across the intricate landscape of neurodegenerative diseases and stroke, presenting a hopeful avenue for improved patient care.

QMrebind: incorporating quantum mechanical force field reparameterization at the ligand binding site for improved drug-target kinetics through milestoning simulations

Understanding the interaction of ligands with biomolecules is an integral component of drug discovery and development. Challenges for computing thermodynamic and kinetic quantities for pharmaceutically relevant receptor-ligand complexes include the size and flexibility of the ligands, large-scale conformational rearrangements of the receptor, accurate force field parameters, simulation efficiency, and sufficient sampling associated with rare events. Our recently developed multiscale milestoning simulation approach, SEEKR2 (Simulation Enabled Estimation of Kinetic Rates v.2), has demonstrated success in predicting unbinding (koff) kinetics by employing molecular dynamics (MD) simulations in regions closer to the binding site. The MD region is further subdivided into smaller Voronoi tessellations to improve the simulation efficiency and parallelization. To date, all MD simulations are run using general molecular mechanics (MM) force fields. The accuracy of calculations can be further improved by incorporating quantum mechanical (QM) methods into generating system-specific force fields through reparameterizing ligand partial charges in the bound state. The force field reparameterization process modifies the potential energy landscape of the bimolecular complex, enabling a more accurate representation of the intermolecular interactions and polarization effects at the bound state. We present QMrebind (Quantum Mechanical force field reparameterization at the receptor-ligand binding site), an ORCA-based software that facilitates reparameterizing the potential energy function within the phase space representing the bound state in a receptor-ligand complex. With SEEKR2 koff estimates and experimentally determined kinetic rates, we compare and interpret the receptor-ligand unbinding kinetics obtained using the newly reparameterized force fields for model host-guest systems and HSP90-inhibitor complexes. This method provides an opportunity to achieve higher accuracy in predicting receptor-ligand koff rate constants.

Direct evaluation of polarity of the ligand binding pocket in retinoid X receptor using a fluorescent solvatochromic agonist

High selectivity of small-molecule drug candidates for their target molecule is important to minimize potential side effects. One factor that contributes to the selectivity is the internal polarity of the ligand-binding pocket (LBP) in the target molecule, but this is difficult to measure. Here, we first confirmed that the retinoid X receptor (RXR) agonist 6-(ethyl(1-isobutyl-2-oxo-4-(trifluoromethyl)-1,2-dihydroquinolin-7-yl)amino)nicotinic acid (NEt-iFQ, 1) exhibits fluorescence solvatochromism, i.e., its Stokes shift depends on the polarity of the solvent, and then we utilized this property to directly measure the internal polarity of the RXRα-LBP. The Stokes shift of 1 when bound to the RXRα-LBP corresponded to that of 1 in chloroform solution. This finding is expected to be helpful for designing RXR-selective ligands. A similar approach should be appliable to evaluate the internal polarity of the LBPs of other receptors.

Structure-activity relationship studies and biological properties evaluation of peptidic NRP-1 ligands: Investigation of N-terminal cysteine importance

Neuropilin-1 (NRP-1) is a major co-receptor of vascular endothelial growth factor receptor-2 (VEGFR-2). It may also stimulate tumour growth and metastasis independently of VEGF-A165. These functions make VEGF-A165/NRP-1 complex formation and its inhibition of great interest, where NRP-1 is the target for which effective ligands are sought. Design of peptide-like inhibitors represent a strategy with great potential in the treatment of NRP-1-related disorders. Here, we present the synthesis, molecular modelling, structure-activity relationship studies as well as biological evaluation of peptides with the branched sequences H2N-X-Lys(hArg)-Dab-Oic-Arg-OH and H2N-Lys(X-hArg)-Dab-Oic-Arg-OH. Two of the designed peptides, in which Cys was inserted in X position, expressed high affinity (∼40 nM value) for NRP-1 and were resistant to enzymatic digestion in human serum. Moreover, peptide/NRP-1 complex promoted fast intracytoplasmic protein trafficking towards the plasma membrane in breast cancer cells. Our results suggest that these compounds might be good candidates for further development of VEGF-A165/NRP-1 inhibitors.

Identification of and Structural Insights into Hit Compounds Targeting N-Myristoyltransferase for Cryptosporidium Drug Development

Each year, approximately 50,000 children under 5 die as a result of diarrhea caused by Cryptosporidium parvum, a protozoan parasite. There are currently no effective drugs or vaccines available to cure or prevent Cryptosporidium infection, and there are limited tools for identifying and validating targets for drug or vaccine development. We previously reported a high throughput screening (HTS) of a large compound library against Plasmodium N-myristoyltransferase (NMT), a validated drug target in multiple protozoan parasite species. To identify molecules that could be effective against Cryptosporidium, we counter-screened hits from the Plasmodium NMT HTS against Cryptosporidium NMT. We identified two potential hit compounds and validated them against CpNMT to determine if NMT might be an attractive drug target also for Cryptosporidium. We tested the compounds against Cryptosporidium using both cell-based and NMT enzymatic assays. We then determined the crystal structure of CpNMT bound to Myristoyl-Coenzyme A (MyrCoA) and structures of ternary complexes with MyrCoA and the hit compounds to identify the ligand binding modes. The binding site architectures display different conformational states in the presence of the two inhibitors and provide a basis for rational design of selective inhibitors.

Development of Kinase-Centric Drugs: A Computational Perspective

Kinases are prominent drug targets in the pharmaceutical and research community due to their involvement in signal transduction, physiological responses, and upon dysregulation, in diseases such as cancer, neurological and autoimmune disorders. Several FDA-approved small-molecule drugs have been developed to combat human diseases since Gleevec was approved for the treatment of chronic myelogenous leukemia. Kinases were considered "undruggable" in the beginning. Several FDA-approved small-molecule drugs have become available in recent years. Most of these drugs target ATP-binding sites, but a few target allosteric sites. Among kinases that belong to the same family, the catalytic domain shows high structural and sequence conservation. Inhibitors of ATP-binding sites can cause off-target binding. Because members of the same family have similar sequences and structural patterns, often complex relationships between kinases and inhibitors are observed. To design and develop drugs with desired selectivity, it is essential to understand the target selectivity for kinase inhibitors. To create new inhibitors with the desired selectivity, several experimental methods have been designed to profile the kinase selectivity of small molecules. Experimental approaches are often expensive, laborious, time-consuming, and limited by the available kinases. Researchers have used computational methodologies to address these limitations in the design and development of effective therapeutics. Many computational methods have been developed over the last few decades, either to complement experimental findings or to forecast kinase inhibitor activity and selectivity. The purpose of this review is to provide insight into recent advances in theoretical/computational approaches for the design of new kinase inhibitors with the desired selectivity and optimization of existing inhibitors.

Molecular Dynamics Simulation Study of the Selective Inhibition of Coagulation Factor IXa over Factor Xa

Thromboembolic disorders, arising from abnormal coagulation, pose a significant risk to human life in the modern world. The FDA has recently approved several anticoagulant drugs targeting factor Xa (FXa) to manage these disorders. However, these drugs have potential side effects, leading to bleeding complications in patients. To mitigate these risks, coagulation factor IXa (FIXa) has emerged as a promising target due to its selective regulation of the intrinsic pathway. Due to the high structural and functional similarities of these coagulation factors and their inhibitor binding modes, designing a selective inhibitor specifically targeting FIXa remains a challenging task. The dynamic behavior of protein-ligand interactions and their impact on selectivity were analyzed using molecular dynamics simulation, considering the availability of potent and selective compounds for both coagulation factors and the co-crystal structures of protein-ligand complexes. Throughout the simulations, we examined ligand movements in the binding site, as well as the contact frequencies and interaction fingerprints, to gain insights into selectivity. Interaction fingerprint (IFP) analysis clearly highlights the crucial role of strong H-bond formation between the ligand and D189 and A190 in the S1 subsite for FIXa selectivity, consistent with our previous study. This dynamic analysis also reveals additional FIXa-specific interactions. Additionally, the absence of polar interactions contributes to the selectivity for FXa, as observed from the dynamic profile of interactions. A contact frequency analysis of the protein-ligand complexes provides further confirmation of the selectivity criteria for FIXa and FXa, as well as criteria for binding and activity. Moreover, a ligand movement analysis reveals key interaction dynamics that highlight the tighter binding of selective ligands to the proteins compared to non-selective and inactive ligands.

SET7, a lysine-specific methyl transferase: An intriguing epigenetic target to combat diabetic nephropathy

Diabetic nephropathy (DN) is a dreadful complication of diabetes that affects ∼50% of diabetics and is a leading cause of end-stage renal disease (ESRD). Studies have linked aberrant expression of lysine methyltransferases (KMTs) to the onset and progression of DN. SET7 is a KMT that methylates specific lysine residues of the histone and nonhistone proteins. It plays an important role in the transforming growth factor-β (TGF-β)-induced upregulation of extracellular matrix (ECM)-associated genes that are responsible for the inflammatory cascade observed in DN. Inhibiting SET7 has potential to attenuate renal disorders in animal studies. This review will focus on the role of SET7 in DN and its potential as a therapeutic target to combat DN.

A DNA-encoded chemical library based on chiral 4-amino-proline enables stereospecific isozyme-selective protein recognition

DNA-encoded chemical libraries (DELs) consist of large chemical compound collections individually linked to DNA barcodes, facilitating pooled construction and screening. However, screening campaigns often fail if the molecular arrangement of the building blocks is not conducive to an efficient interaction with a protein target. Here we postulated that the use of rigid, compact and stereo-defined central scaffolds for DEL synthesis may facilitate the discovery of very specific ligands capable of discriminating between closely related protein targets. We synthesized a DEL comprising 3,735,936 members, featuring the four stereoisomers of 4-aminopyrrolidine-2-carboxylic acid as central scaffolds. The library was screened in comparative selections against pharmaceutically relevant targets and their closely related protein isoforms. Hit validation results revealed a strong impact of stereochemistry, with large affinity differences between stereoisomers. We identified potent isozyme-selective ligands against multiple protein targets. Some of these hits, specific to tumour-associated antigens, demonstrated tumour-selective targeting in vitro and in vivo. Collectively, constructing DELs with stereo-defined elements contributed to high library productivity and ligand selectivity.

Analyzing Active Compounds in Elateriospermum tapos Yogurt for Maternal Obesity: A Network Pharmacology and Molecular Docking Study

Maternal obesity, characterized by an elevated body mass index (BMI) during pregnancy, is known to have adverse effects on the offspring. However, a recent study suggests that Elateriospermum tapos (E. tapos) yogurt may hold potential in mitigating excessive weight retention post-pregnancy. Thus, this study aims to employ network pharmacology to explore the pharmacological effects of the bioactive compounds present in E. tapos yogurt against maternal obesity. Initially, a screening process is conducted to identify the bioactive compounds in E. tapos yogurt, followed by the prediction of potential gene targets for these compounds using Swiss Target Prediction and the SuperPred databases. Maternal obesity-associated genes are sourced from the OMIM, DisGeNet, and GeneCards databases. The interaction between the identified compounds and maternal obesity genes is established via protein-protein interaction analysis, gene ontology examination, and KEGG pathway analysis. To validate the results, molecular docking studies are conducted using AutoDock Tools software. The findings reveal that out of the 64 compounds analyzed, three meet the screening criteria, resulting in a total of 380 potential gene targets. Among these targets, 240 are shared with maternal obesity-related genes. Further analysis demonstrates the favorable affinity of these active compounds with key targets, linking them to biological processes involving protein phosphorylation, inflammation, as well as the pathways related to lipid metabolism, atherosclerosis, and the other signaling pathways. In conclusion, this study provides valuable insights into the potential pharmacological effects of the bioactive compounds found in E. tapos yogurt against maternal obesity. These findings open avenues for further exploration and potential therapeutic interventions targeting maternal obesity.

Development of Long-Acting Dipeptidyl Peptidase-4 Inhibitors: Structural Evolution and Long-Acting Determinants

Considerable effort has been made to achieve less frequent dosing in the development of DPP-4 inhibitors. Enthusiasm for long-acting DPP-4 inhibitors is based on the promise that such agents with less frequent dosing regimens are associated with improved patient adherence, but the rational design of long-acting DPP-4 inhibitors remains a major challenge. In this Perspective, the development of long-acting DPP-4 inhibitors is comprehensively summarized to highlight the evolution of initial lead compounds on the path toward developing long-acting DPP-4 inhibitors over nearly three decades. The determinants for long duration of action are then examined, including the nature of the target, potency, binding kinetics, crystal structures, selectivity, and preclinical and clinical pharmacokinetic and pharmacodynamic profiles. More importantly, several possible approaches for the rational design of long-acting drugs are discussed. We hope that this information will facilitate the design and development of safer and more effective long-acting DPP-4 inhibitors and other oral drugs.

In silico and in vitro inhibition of host-based viral entry targets and cytokine storm in COVID-19 by ginsenoside compound K

SARS-CoV-2 is a novel coronavirus that emerged as an epidemic, causing a respiratory disease with multiple severe symptoms and deadly consequences. ACE-2 and TMPRSS2 play crucial and synergistic roles in the membrane fusion and viral entry of SARS-CoV-2 (COVID-19). The spike (S) protein of SARS-CoV-2 binds to the ACE-2 receptor for viral entry, while TMPRSS2 proteolytically cleaves the S protein into S1 and S2 subunits, promoting membrane fusion. Therefore, ACE-2 and TMPRSS2 are potential drug targets for treating COVID-19, and their inhibition is a promising strategy for treatment and prevention. This study proposes that ginsenoside compound K (G-CK), a triterpenoid saponin abundant in Panax Ginseng, a dietary and medicinal herb highly consumed in Korea and China, effectively binds to and inhibits ACE-2 and TMPRSS2 expression. We initially conducted an in-silico evaluation where G-CK showed a high affinity for the binding sites of the two target proteins of SARS-CoV-2. Additionally, we evaluated the stability of G-CK using molecular dynamics (MD) simulations for 100 ns, followed by MM-PBSA calculations. The MD simulations and free energy calculations revealed that G-CK has stable and favorable energies, leading to strong binding with the targets. Furthermore, G-CK suppressed ACE2 and TMPRSS2 mRNA expression in A549, Caco-2, and MCF7 cells at a concentration of 12.5 μg/mL and in LPS-induced RAW 264.7 cells at a concentration of 6.5 μg/mL, without significant cytotoxicity.ACE2 and TMPRSS2 expression were significantly lower in A549 and RAW 264.7 cells following G-CK treatment. These findings suggest that G-CK may evolve as a promising therapeutic against COVID-19.

In silico identification of modulators of J domain protein-Hsp70 interactions in Plasmodium falciparum: a drug repurposing strategy against malaria

Plasmodium falciparum is a unicellular, intracellular protozoan parasite, and the causative agent of malaria in humans, a deadly vector borne infectious disease. A key phase of malaria pathology, is the invasion of human erythrocytes, resulting in drastic remodeling by exported parasite proteins, including molecular chaperones and co-chaperones. The survival of the parasite within the human host is mediated by P. falciparum heat shock protein 70s (PfHsp70s) and J domain proteins (PfJDPs), functioning as chaperones-co-chaperones partnerships. Two complexes have been shown to be important for survival and pathology of the malaria parasite: PfHsp70-x-PFE0055c (exported); and PfHsp70-2-PfSec63 (endoplasmic reticulum). Virtual screening was conducted on the drug repurposing library, the Pandemic Response Box, to identify small-molecules that could specifically disrupt these chaperone complexes. Five top ranked compounds possessing preferential binding affinity for the malarial chaperone system compared to the human system, were identified; three top PfHsp70-PfJDP binders, MBX 1641, zoliflodacin and itraconazole; and two top J domain binders, ezetimibe and a benzo-diazepinone. These compounds were validated by repeat molecular dockings and molecular dynamics simulation, resulting in all the compounds, except for MBX 1461, being confirmed to bind preferentially to the malarial chaperone system. A detailed contact analysis of the PfHsp70-PfJDP binders identified two different types of modulators, those that potentially inhibit complex formation (MBX 1461), and those that potentially stabilize the complex (zoliflodacin and itraconazole). These data suggested that zoliflodacin and itraconazole are potential novel modulators specific to the malarial system. A detailed contact analysis of the J domain binders (ezetimibe and the benzo-diazepinone), revealed that they bound with not only greater affinity but also a better pose to the malarial J domain compared to that of the human system. These data suggested that ezetimibe and the benzo-diazepinone are potential specific inhibitors of the malarial chaperone system. Both itraconazole and ezetimibe are FDA-approved drugs, possess anti-malarial activity and have recently been repurposed for the treatment of cancer. This is the first time that such drug-like compounds have been identified as potential modulators of PfHsp70-PfJDP complexes, and they represent novel candidates for validation and development into anti-malarial drugs.

The therapeutic potential of sphingolipids for cardiovascular diseases

Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality worldwide and Inflammation plays a critical role in the development of CVD. Despite considerable progress in understanding the underlying mechanisms and various treatment options available, significant gaps in therapy necessitate the identification of novel therapeutic targets. Sphingolipids are a family of lipids that have gained attention in recent years as important players in CVDs and the inflammatory processes that underlie their development. As preclinical studies have shown that targeting sphingolipids can modulate inflammation and ameliorate CVDs, targeting sphingolipids has emerged as a promising therapeutic strategy. This review discusses the current understanding of sphingolipids' involvement in inflammation and cardiovascular diseases, the existing therapeutic approaches and gaps in therapy, and explores the potential of sphingolipids-based drugs as a future avenue for CVD treatment.

μMap Photoproximity Labeling Enables Small Molecule Binding Site Mapping

The characterization of ligand binding modes is a crucial step in the drug discovery process and is especially important in campaigns arising from phenotypic screening, where the protein target and binding mode are unknown at the outset. Elucidation of target binding regions is typically achieved by X-ray crystallography or photoaffinity labeling (PAL) approaches; yet, these methods present significant challenges. X-ray crystallography is a mainstay technique that has revolutionized drug discovery, but in many cases structural characterization is challenging or impossible. PAL has also enabled binding site mapping with peptide- and amino-acid-level resolution; however, the stoichiometric activation mode can lead to poor signal and coverage of the resident binding pocket. Additionally, each PAL probe can have its own fragmentation pattern, complicating the analysis by mass spectrometry. Here, we establish a robust and general photocatalytic approach toward the mapping of protein binding sites, which we define as identification of residues proximal to the ligand binding pocket. By utilizing a catalytic mode of activation, we obtain sets of labeled amino acids in the proximity of the target protein binding site. We use this methodology to map, in vitro, the binding sites of six protein targets, including several kinases and molecular glue targets, and furthermore to investigate the binding site of the STAT3 inhibitor MM-206, a ligand with no known crystal structure. Finally, we demonstrate the successful mapping of drug binding sites in live cells. These results establish μMap as a powerful method for the generation of amino-acid- and peptide-level target engagement data.

Excited State Dynamics of Alizarin Red S Nanoparticles in Solution

Alizarin red S is a sulfonated, water-soluble derivative of alizarin. This work presents femtosecond studies of alizarin red S (ARS) nanoparticles in comparison to ARS in aqueous solution and to alizarin in DMSO. The femtosecond studies cover a probing spectral range of 350-750 nm using different excitation wavelengths, taking into account the variation of the absorption spectra with the pH values of the solvent. Stationary absorption spectra show slight differences between solution and nanoparticles. Excitation at 530 nm results in low and noisy responses, therefore, we additionally recorded transient spectra of the nanoparticles at λex = 267 nm. While the results in DMSO are comparable to previous studies in non-aqueous solvents, we report a relatively fast relaxation of 14 ps in [La(OH)2][ARS] nanoparticles in aqueous solution after excitation at 530 nm, which is similar to Na(ARS) solution (19 ps). The dynamics changed with lower pH, but still without significant differences between nanoparticles and solution. We propose [La(OH)2][ARS] nanoparticles as a suitable alternative to dissolved molecules with similar spectroscopic properties, for example, with regard to biomarker applications.

Structural basis of efficacy-driven ligand selectivity at GPCRs

A drug's selectivity for target receptors is essential to its therapeutic utility, but achieving selectivity between similar receptors is challenging. The serendipitous discovery of ligands that stimulate target receptors more strongly than closely related receptors, despite binding with similar affinities, suggests a solution. The molecular mechanism of such 'efficacy-driven selectivity' has remained unclear, however, hindering design of such ligands. Here, using atomic-level simulations, we reveal the structural basis for the efficacy-driven selectivity of a long-studied clinical drug candidate, xanomeline, between closely related muscarinic acetylcholine receptors (mAChRs). Xanomeline's binding mode is similar across mAChRs in their inactive states but differs between mAChRs in their active states, with divergent effects on active-state stability. We validate this mechanism experimentally and use it to design ligands with altered efficacy-driven selectivity. Our results suggest strategies for the rational design of ligands that achieve efficacy-driven selectivity for many pharmaceutically important G-protein-coupled receptors.

In silico design and computational evaluation of novel 2-arylaminopyrimidine-based compounds as potential multi-targeted protein kinase inhibitors: application for the native and mutant (T315I) Bcr-Abl tyrosine kinase

An integrated computational approach to drug discovery was used to identify novel potential inhibitors of the native and mutant (T315I) Bcr-Abl tyrosine kinase, the enzyme playing a key role in the pathogenesis of chronic myeloid leukemia (CML). This approach included i) design of chimeric molecules based on the 2-arylaminopyrimidine fragment, the main pharmacophore of the Abl kinase inhibitors imatinib and nilotinib used in the clinic for the CML treatment, ii) molecular docking of these compounds with the ATP-binding site of the native and mutant Abl kinase, iii) refinement of the ligand-binding poses by the quantum chemical method PM7, iv) molecular dynamics simulations of the ligand/Abl complexes, and v) prediction of the ligand/Abl binding affinity in terms of scoring functions of molecular docking, machine learning, quantum chemistry, and molecular dynamics. As a result, five top-ranking compounds able to effectively block the enzyme catalytic site were identified. According to the data obtained, these compounds exhibit close modes of binding to the Abl kinase active site that are mainly provided by hydrogen bonds and multiple van der Waals contacts. The identified compounds show high binding affinity to the native and mutant Abl kinase comparable with the one calculated for the FDA-approved kinase-targeted inhibitors imatinib, nilotinib, and ponatinib used in the calculations as a positive control. The results obtained testify to the predicted drug candidates against CML may serve as good scaffolds for the design of novel anticancer agents able to target the ATP-binding pocket of the native and mutant Abl kinase.Communicated by Ramaswamy H. Sarma.

The human neurosecretome: extracellular vesicles and particles (EVPs) of the brain for intercellular communication, therapy, and liquid-biopsy applications

Emerging evidence suggests that brain derived extracellular vesicles (EVs) and particles (EPs) can cross blood-brain barrier and mediate communication among neurons, astrocytes, microglial, and other cells of the central nervous system (CNS). Yet, a complete understanding of the molecular landscape and function of circulating EVs & EPs (EVPs) remain a major gap in knowledge. This is mainly due to the lack of technologies to isolate and separate all EVPs of heterogeneous dimensions and low buoyant density. In this review, we aim to provide a comprehensive understanding of the neurosecretome, including the extracellular vesicles that carry the molecular signature of the brain in both its microenvironment and the systemic circulation. We discuss the biogenesis of EVPs, their function, cell-to-cell communication, past and emerging isolation technologies, therapeutics, and liquid-biopsy applications. It is important to highlight that the landscape of EVPs is in a constant state of evolution; hence, we not only discuss the past literature and current landscape of the EVPs, but we also speculate as to how novel EVPs may contribute to the etiology of addiction, depression, psychiatric, neurodegenerative diseases, and aid in the real time monitoring of the "living brain". Overall, the neurosecretome is a concept we introduce here to embody the compendium of circulating particles of the brain for their function and disease pathogenesis. Finally, for the purpose of inclusion of all extracellular particles, we have used the term EVPs as defined by the International Society of Extracellular Vesicles (ISEV).

In Silico-Motivated Discovery of Novel Potent Glycogen Synthase-3 Inhibitors: 1-(Alkyl/arylamino)-3H-naphtho[1,2,3-de]quinoline-2,7-dione Identified as a Scaffold for Kinase Inhibitor Development

Glycogen synthase kinase-3 (GSK-3) isoforms α and β have diverse roles within cell biology, and have been linked with multiple diseases that include prominent CNS conditions such as Alzheimer's disease and several psychiatric disorders. In this study, motivated by computation, we aimed to identify novel ATP-binding site inhibitors of GSK-3 with CNS-active potential. A ligand screening (docking) protocol against GSK-3β was first optimized, employing an active/decoy benchmarking set, with the final protocol selected based on statistical performance analysis. The optimized protocol involved pre-filtering of ligands using a three-point 3D-pharmacophore, followed by Glide-SP docking applying hinge region hydrogen bonding constraints. Using this approach, the Biogenic subset of the ZINC15 compound database was screened, focused on compounds with potential for CNS-activity. Twelve compounds (generation I) were selected for experimental validation using in vitro GSK-3β binding assays. Two hit compounds, 1 and 2, with 6-amino-7H-benzo[e]perimidin-7-one and 1-(phenylamino)-3H-naphtho[1,2,3-de]quinoline-2,7-dione type scaffolds were identified with IC₅₀ values of 1.63 µM and 20.55 µM, respectively. Ten analogues of 2 (generation II) were selected for structure activity relationship (SAR) analysis and revealed four low micromolar inhibitors (<10 µM), with 19 (IC₅₀ = 4.1 µM)~five times more potent than initial hit compound 2. Selectivity screening of low micromolar inhibitors 14 and 19 (comparing aryl- and alkyl-substituents) against 10 homologous kinases revealed unique selectivity profiles, with both compounds more potent against the GSK-3α isoform (IC₅₀s~2 µM) and, additionally, inhibitors of PKBβ (IC₅₀s < 25 µM). Compound 14 also inhibited ERK2 and 19, PKCγ, but generally good selectivity for GSK-3 isoforms over the other kinases was observed. The compounds had excellent predicted oral bioavailability and CNS-activity profiles, presenting promising candidates for future testing in cellular models of disease.

A novel class of phenylpyrazolone-sulphonamides rigid synthetic anticancer molecules selectively inhibit the isoform IX of carbonic anhydrases guided by molecular docking and orbital analyses

All the previously reported phenylpyrazoles as carbonic anhydrase inhibitors (CAIs) were found to have small sizes and high levels of flexibility, and hence showed low selectivity profiles toward a particular isoform of CA. Herein, we report the development of a more rigid ring system bearing a sulfonamide hydrophilic head and a lipophilic tail to develop novel molecules that are suggested to have a better selectivity toward a special CA isoform. Accordingly, three novel sets of pyrano[2,3-c]pyrazoles attached with sulfonamide head and aryl hydrophobic tail were synthesized to enhance the selectivity toward a specific isoform of human carbonic anhydrases (hCAs). The impact of both attachments on the potency and selectivity has been extensively discussed in terms of in vitro cytotoxicity evaluation under hypoxic conditions, structure-activity relationship and carbonic anhydrase enzyme assay. All of the new candidates displayed good cytotoxic activities against breast and colorectal carcinomas. Results of the carbonic anhydrase enzyme assay demonstrated the preferential of compounds 22, 24 and 27 to inhibit the isoform IX of hCAs selectively. Wound-healing assay has also been performed and revealed the potential of 27 to decrease the wound closure percentage in MCF-7 cells. Molecular docking and molecular orbital analysis have finally been conducted. Results indicate the potential binding interactions of 24 and 27 with several crucial amino acids of the hCA IX.Communicated by Ramaswamy H. Sarma.

A review on cyclin-dependent kinase 5: An emerging drug target for neurodegenerative diseases

Cyclin-dependent kinase 5 (CDK5) is the serine/threonine-directed kinase mainly found in the brain and plays a significant role in developing the central nervous system. Recent evidence suggests that CDK5 is activated by specific cyclins regulating its expression and activity. P35 and p39 activate CDK5, and their proteolytic degradation produces p25 and p29, which are stable products involved in the hyperphosphorylation of tau protein, a significant hallmark of various neurological diseases. Numerous high-affinity inhibitors of CDK5 have been designed, and some are marketed drugs. Roscovitine, like other drugs, is being used to minimize neurological symptoms. Here, we performed an extensive literature analysis to highlight the role of CDK5 in neurons, synaptic plasticity, DNA damage repair, cell cycle, etc. We have investigated the structural features of CDK5, and their binding mode with the designed inhibitors is discussed in detail to develop attractive strategies in the therapeutic targeting of CDK5 for neurodegenerative diseases. This review provides deeper mechanistic insights into the therapeutic potential of CDK5 inhibitors and their implications in the clinical management of neurodegenerative diseases.

Structure-Based Design of Glycosylated Oxytocin Analogues with Improved Selectivity and Antinociceptive Activity

Acute and chronic pain is often treated with opioids despite the negative side effects of constipation, physical dependence, respiratory depression, and overdose. The misuse of opioid analgesics has given rise to the opioid crisis/epidemic, and alternate nonaddictive analgesics are urgently needed. Oxytocin, a pituitary hormone, is an alternative to the small molecule treatments available and has been used as an analgesic as well as for the treatment and prevention of opioid use disorder (OUD). Clinical implementation is limited by its poor pharmacokinetic profile, a result of the labile disulfide bond between two cysteine residues in the native sequence. Stable brain penetrant oxytocin analogues have been synthesized by replacement of the disulfide bond with a stable lactam and glycosidation of the C-terminus. These analogues show exquisite selectivity for the oxytocin receptor and potent in vivo antinociception in mice following peripheral (i.v.) administration, supporting further study of their clinical potential.

Insightful Improvement in the Design of Potent Uropathogenic E. coli FimH Antagonists

Selective antiadhesion antagonists of Uropathogenic Escherichia coli (UPEC) type-1 Fimbrial adhesin (FimH) are attractive alternatives for antibiotic therapies and prophylaxes against acute or recurrent urinary tract infections (UTIs) caused by UPECs. A rational small library of FimH antagonists based on previously described C-linked allyl α-D-mannopyranoside was synthesized using Heck cross-coupling reaction using a series of iodoaryl derivatives. This work reports two new members of FimH antagonist amongst the above family with sub nanomolar affinity. The resulting hydrophobic aglycones, including constrained alkene and aryl groups, were designed to provide additional favorable binding interactions with the so-called FimH "tyrosine gate". The newly synthesized C-linked glycomimetic antagonists, having a hydrolytically stable anomeric linkage, exhibited improved binding when compared to previously published analogs, as demonstrated by affinity measurement through interactions by FimH lectin. The crystal structure of FimH co-crystallized with one of the nanomolar antagonists revealed the binding mode of this inhibitor into the active site of the tyrosine gate. In addition, selected mannopyranoside constructs neither affected bacterial growth or cell viability nor interfered with antibiotic activity. C-linked mannoside antagonists were effective in decreasing bacterial adhesion to human bladder epithelial cells (HTB-9). Therefore, these molecules constituted additional therapeutic candidates' worth further development in the search for potent anti-adhesive drugs against infections caused by UPEC.

Epitope imprinted polymeric materials: application in electrochemical detection of disease biomarkers

Epitope imprinting is a promising method for creating specialized recognition sites that resemble natural biorecognition elements. Epitope-imprinted materials have gained a lot of attention recently in a variety of fields, including bioanalysis, drug delivery, and clinical therapy. The vast applications of epitope imprinted polymers are due to the flexibility in choosing monomers, the simplicity in obtaining templates, specificity toward targets, and resistance to harsh environments along with being cost effective in nature. The "epitope imprinting technique," which uses only a tiny subunit of the target as the template during imprinting, offers a way around various drawbacks inherent to biomacromolecule systems i.e., traditional molecular imprinting techniques with regards to the large size of proteins, such as the size, complexity, accessibility, and conformational flexibility of the template. Electrochemical based sensors are proven to be promising tool for the quick, real-time monitoring of biomarkers. This review unravels epitope imprinting techniques, approaches, and strategies and highlights the applicability of these techniques for the electrochemical quantification of biomarkers for timely disease monitoring. In addition, some challenges are discussed along with future prospective developments.

Accessing Transient Binding Pockets by Protein Engineering and Yeast Surface Display Screening

The binding pocket of some therapeutic targets can acquire multiple conformations that, to some extent, depend on the protein dynamics and the interaction with other molecules. The inability to reach the binding pocket can impose a substantial or even insurmountable barrier for the de novo identification or optimization of small-molecule ligands. Herein, we describe a protocol for the engineering of a target protein and a yeast display FACS sorting strategy to identify protein variants with a stable transient binding pocket with improved binding for a cryptic site-specific ligand. This strategy may facilitate drug discovery using the resulting protein variants with accessible binding pockets for ligand screening.

Synthesis of benzylated amine-substituted xanthone derivatives and their antioxidant and anti-inflammatory activities

Oxidative stress and its constant companion, inflammation, play a critical part in the pathogenesis of many acute and chronic illnesses. The discovery of new multi-targeted drug candidates with antioxidant and anti-inflammatory properties is deemed necessary. Thus, a series of novel xanthone derivatives with halogenated benzyl (4b-4d, 4f-4h) and methoxylated benzyl groups (4e) attached to the butoxy amine substituent were synthesized in this study. The synthesized xanthone derivatives exhibited stronger antioxidant activity against H₂ O₂ scavenging than the standard drug, α-tocopherol, but weaker towards DPPH scavenging and ferrous ion chelation. Besides that, 4b-4d, 4f-4h demonstrated good anti-inflammatory activities through NO production inhibition towards lipopolysaccharide (LPS)-induced RAW 264.7 cells and showed 2-4 times stronger effects than the standard drug, diclofenac sodium. Moreover, compound 4b with two brominated benzyl groups attached to the butoxy amine substituent suppressed the production of pro-inflammatory cytokines, TNF-α and IL-1β, significantly. Structure-activity relationship elucidated that the halogenated benzylamine substituent plays an important role in contributing the antioxidant and anti-inflammatory activities of xanthones. In summary, xanthone 4b was identified as a potential lead compound to be further developed into antioxidant and anti-inflammatory drugs. Thus, further studies on the related mechanisms of action of 4b are recommended.

Protein-ligand binding affinity prediction with edge awareness and supervised attention

Accurate prediction of protein-ligand binding affinity is crucial in structure-based drug design but remains some challenges even with recent advances in deep learning: (1) Existing methods neglect the edge information in protein and ligand structure data; (2) current attention mechanisms struggle to capture true binding interactions in the small dataset. Herein, we proposed SEGSA_DTA, a SuperEdge Graph convolution-based and Supervised Attention-based Drug-Target Affinity prediction method, where the super edge graph convolution can comprehensively utilize node and edge information and the multi-supervised attention module can efficiently learn the attention distribution consistent with real protein-ligand interactions. Results on the multiple datasets show that SEGSA_DTA outperforms current state-of-the-art methods. We also applied SEGSA_DTA in repurposing FDA-approved drugs to identify potential coronavirus disease 2019 (COVID-19) treatments. Besides, by using SHapley Additive exPlanations (SHAP), we found that SEGSA_DTA is interpretable and further provides a new quantitative analytical solution for structure-based lead optimization.

An interpretable machine learning model for selectivity of small molecules against homologous protein family

Aim: In the early stages of drug discovery, various experimental and computational methods are used to measure the specificity of small molecules against a target protein. The selectivity of small molecules remains a challenge leading to off-target side effects. Methods: We have developed a multitask deep learning model for predicting the selectivity on closely related homologs of the target protein. The model has been tested on the Janus-activated kinase and dopamine receptor families of proteins. Results & conclusion: The feature-based representation (extended connectivity fingerprint 4) with Extreme Gradient Boosting performed better when compared with deep neural network models in most of the evaluation metrics. Both the Extreme Gradient Boosting and deep neural network models outperformed the graph-based models. Furthermore, to decipher the model decision on selectivity, the important fragments associated with each homologous protein were identified.

Acyldepsipeptide Analogues: A Future Generation Antibiotics for Tuberculosis Treatment

Acyldepsipeptides (ADEPs) are a new class of emerging antimicrobial peptides (AMPs), which are currently explored for treatment of pathogenic infections, including tuberculosis (TB). These cyclic hydrophobic peptides have a unique bacterial target to the conventional anti-TB drugs, and present a therapeutic window to overcome Mycobacterium Tuberculosis (M. tb) drug resistance. ADEPs exerts their antibacterial activity on M. tb strains through activation of the protein homeostatic regulatory protease, the caseinolytic protease (ClpP1P2). ClpP1P2 is normally regulated and activated by the ClpP-ATPases to degrade misfolded and toxic peptides and/or short proteins. ADEPs bind and dysregulate all the homeostatic capabilities of ClpP1P2 while inducing non-selective proteolysis. The uncontrolled proteolysis leads to M. tb cell death within the host. ADEPs analogues that have been tested possess cytotoxicity and poor pharmacokinetic and pharmacodynamic properties. However, these can be improved by drug design techniques. Moreover, the use of nanomaterial in conjunction with ADEPs would yield effective synergistic effect. This new mode of action has potential to combat and eradicate the extensive multi-drug resistance (MDR) problem that is currently faced by the public health pertaining bacterial infections, especially TB.

Modified Electrostatic Complementary Score Function and Its Application Boundary Exploration in Drug Design

In recent years, machine learning (ML) models have been found to quickly predict various molecular properties with accuracy comparable to high-level quantum chemistry methods. One such example is the calculation of electrostatic potential (ESP). Different ESP prediction ML models were proposed to generate surface molecular charge distribution. Electrostatic complementarity (EC) can apply ESP data to quantify the complementarity between a ligand and its binding pocket, leading to the potential to increase the efficiency of drug design. However, there is not much research discussing EC score functions and their applicability domain. We propose a new EC score function modified from the one originally developed by Bauer and Mackey, and confirm its effectiveness against the available Pearson's R correlation coefficient. Additionally, the applicability domain of the EC score and two indices used to define the EC score application scope will be discussed.

The mechanism of action and clinical value of PROTACs: A graphical review

The use of small molecule drugs to inhibit active protein targets has revolutionised the treatment options for many diseases in the past 30 years. The greatly improved pharmacokinetic properties of modern drugs combined with enhanced cell permeability and oral bioavailability has made these molecules ideal for reaching protein targets of interest in cells and inhibiting disease-driven signalling pathways. However, these small molecule drugs have several limitations which have opened the doors for the development of a new class of compounds, known as proteolysis targeting chimeras (PROTACs). These next generation drugs actively and specifically degrade designated protein targets and hold the potential to greatly expand the druggable genome, including previously drug-resistant targets.