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Schistosomiasis, a neglected tropical disease caused by flatworms of the Schistosoma genus, can lead to severe health complications such as Hepatosplenomegaly, intestinal cancer, anemia, and heightened susceptibility to other infections like HIV. With limited treatment options, especially given the potential rise of drug resistance to the sole available drug, Praziquantel (PZQ), there is an imperative to discover novel and effective drugs against Schistosomiasis. In regards, a computational molecular modelling strategy was employed, with a specific focus on Schistosoma mansoni Thioredoxin glutathione reductase (SmTGR). Using structure-based drug design, we searched the ChEMBL database for lead compounds, identifying Compound 34 with a remarkable binding score of −167.014 kcal mol−1. Six new potent compounds were designed with enhanced binding (−169.451 to −181.579 kcal mol−1) and predicted activities (6.082–6.765) compared to both the lead compound and the standard drug, PZQ. Rigorous validation through molecular dynamics (MD) simulations confirmed the stability and integrity of the docked complexes. An evaluation of drug-likeness and ADMET characteristics revealed that the newly developed analogues possess favourable pharmacokinetic profiles, adhering to Lipinski's rule of five. In addition, the synergistic insights gained from density functional theory (DFT) analyses further underscore the structural diversity and potential applications of these derivatives. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease is a neurological disorder marked by the accumulation of amyloid beta (Aβ) aggregates, resulting from mutations in the amyloid precursor protein. The enzyme β-secretase, also known as β-site amyloid precursor protein cleaving enzyme 1 (BACE1), plays a crucial role in generating Aβ peptides. With no targeted therapy available for Alzheimer’s disease, inhibiting BACE1 aspartic protease has emerged as a primary treatment target. Since 1999, compounds demonstrating potential binding to the BACE1 receptor have advanced to human trials. Structural optimization of synthetically derived compounds, coupled with computational approaches, has offered valuable insights for developing highly selective leads with drug-like properties. This review highlights pivotal studies on the design and development of BACE1 inhibitors as anti-Alzheimer’s disease agents. It summarizes computational methods employed in facilitating drug discovery for potential BACE1 inhibitors and provides an update on their clinical status, indicating future directions for novel BACE1 inhibitors. The promising clinical results of Elenbecestat (E-2609) catalyze the development of effective, selective BACE1 inhibitors in the future.

Access to small, rigid, and sp3‐rich molecules is a major limitation in the drug discovery for challenging protein targets. FK506‐binding proteins hold high potential as drug targets or enablers of molecular glues but are fastidious in the chemotypes accepted as ligands. We here report an enantioselective synthesis of a highly rigidified pipecolate‐mimicking tricyclic scaffold that precisely positions functional groups for interacting with FKBPs. This was enabled by a 14‐step gram‐scale synthesis featuring anodic oxidation, stereospecific vinylation, and N‐acyl iminium cyclization. Structure‐based optimization resulted in the discovery of FKBP inhibitors with picomolar biochemical and subnanomolar cellular activity that represent the most potent FKBP ligands known to date. [ABSTRACT FROM AUTHOR]

Clostridium histolyticum is a Gram-positive anaerobic bacterium belonging to the Clostridium genus. It produces collagenase, an enzyme involved in breaking down collagen which is a key component of connective tissues. However, antimicrobial resistance (AMR) poses a great challenge in combating infections caused by this bacteria. The lengthy nature of traditional drug development techniques has resulted in a shift to computer-aided drug design and other modern drug discovery approaches. The above method offers a cost-effective means for gathering comprehensive information about how ligands interact with their target proteins. The objective of this study is to create novel, explicit drugs that specifically inhibit the C. histolyticum collagenase enzyme. Through structure-based virtual screening, a library containing 1830 compounds was screened to identify potential drug candidates against collagenase enzymes. Following that, molecular dynamic (MD) simulation was performed in an aqueous solution to evaluate the behavior of protein and ligand in a dynamic environment while density functional theory (DFT) analysis was executed to predict the molecular properties and structure of lead compounds, and the WaterSwap technique was utilized to obtain insights into the drug–protein interaction with water molecules. Furthermore, principal component analysis (PCA) was performed to reveal conformational changes, salt bridges to express electrostatic interaction and protein stability, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) to assess the pharmacokinetics profile of top compounds and control molecules. Three potent drug candidates were identified MSID000001, MSID000002, MSID000003, and the control with a binding score of −10.7 kcal/mol, −9.8 kcal/mol, −9.5 kcal/mol, and −8 kcal/mol, respectively. Furthermore, Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) analysis of the simulation trajectories revealed energy scores of −79.54 kcal/mol, −73.99 kcal/mol, −62.26 kcal/mol, and −70.66 kcal/mol, correspondingly. The pharmacokinetics properties exhibited were under the acceptable range. The compounds hold the potential to be novel drugs; therefore, further investigation needs to be conducted to find out their anti-collagenase action against C. histolyticum infections and antibiotic resistance. [ABSTRACT FROM AUTHOR]

Cancer is one of the life-threatening diseases and the second leading cause of death in the world. The estrogen receptor can be considered as one of the significant drug targets for cancer. A large number of clinically used anticancer drugs were identified from phytochemicals. Multiple literatures suggested that extracts of Datura sp. significantly inhibit estrogen receptors associated with human cancer. In the present study, all reported natural products present in Datura sp. were subjected to molecular docking against estrogen receptors. The top hits were shortlisted based on binding orientation and docking score and subjected to molecular dynamics simulation to explore the conformational stability followed by binding energy calculation. The ligand [(1S,5R)-8-Methyl-8-Azabicyclo [3.2.1] Octan-3-yl] (2R)-3-Hydroxy-2-Phenylpropanoate depicts highly acceptable MD simulations outcomes and drug-likeness profile. Knowledge-based de novo design and similar ligand screening were executed using the structural information. The designed ligand DL-50 exhibited satisfactory binding, drug-likeness profile, and well-accepted ADMET profile followed by easy synthetic accessibility which further requires experimental validation. [ABSTRACT FROM AUTHOR]

This review aims to highlight the structures of ADAR proteins that have been crucial in the discernment of their functions and are relevant to future therapeutic development. ADAR proteins can correct or diversify genetic information, underscoring their pivotal contribution to protein diversity and the sophistication of neuronal networks. ADAR proteins have numerous functions in RNA editing independent roles and through the mechanisms of A-I RNA editing that continue to be revealed. Provided is a detailed examination of the ADAR family members—ADAR1, ADAR2, and ADAR3—each characterized by distinct isoforms that offer both structural diversity and functional variability, significantly affecting RNA editing mechanisms and exhibiting tissue-specific regulatory patterns, highlighting their shared features, such as double-stranded RNA binding domains (dsRBD) and a catalytic deaminase domain (CDD). Moreover, it explores ADARs’ extensive roles in immunity, RNA interference, and disease modulation, demonstrating their ambivalent nature in both the advancement and inhibition of diseases. Through this comprehensive analysis, the review seeks to underline the potential of targeting ADAR proteins in therapeutic strategies, urging continued investigation into their biological mechanisms and health implications.

The filamentous fungus Magnaporthe oryzae is widely recognized as a notorious plant pathogen responsible for causing rice blasts. With rapid advancements in molecular biology technologies, numerous regulatory mechanisms have been thoroughly investigated. However, most recent studies have predominantly focused on infection-related pathways or host defence mechanisms, which may be insufficient for developing novel structure-based prevention strategies. A substantial body of literature has utilized cryo-electron microscopy and X-ray diffraction to explore the relationships between functional components, shedding light on the identification of potential drug targets. Owing to the complexity of protein extraction and stochastic nature of crystallization, obtaining high-quality structures remains a significant challenge for the scientific community. Emerging computational tools such as AlphaFold for structural prediction, docking for interaction analysis, and molecular dynamics simulations to replicate in vivo conditions provide novel avenues for overcoming these challenges. In this review, we aim to consolidate the structural biological advancements in M. oryzae, drawing upon mature experimental experiences from other species such as Saccharomyces cerevisiae and mammals. We aim to explore the potential of protein construction to address the invasion and proliferation of M. oryzae, with the goal of identifying new drug targets and designing small-molecule compounds to manage this disease.

Traditional drug discovery struggles to keep pace with the ever-evolving threat of infectious diseases. New viruses and antibiotic-resistant bacteria, all demand rapid solutions. Artificial Intelligence (AI) offers a promising path forward through accelerated drug repurposing. AI allows researchers to analyze massive datasets, revealing hidden connections between existing drugs, disease targets, and potential treatments. This approach boasts several advantages. First, repurposing existing drugs leverages established safety data and reduces development time and costs. Second, AI can broaden the search for effective therapies by identifying unexpected connections between drugs and potential new targets. Finally, AI can help mitigate limitations by predicting and minimizing side effects, optimizing drugs for repurposing, and navigating intellectual property hurdles. The article explores specific AI strategies like virtual screening, target identification, structure base drug design and natural language processing. Real-world examples highlight the potential of AI-driven drug repurposing in discovering new treatments for infectious diseases.

The PAX8/PPARγ rearrangement, producing the PAX8–PPARγ fusion protein (PPFP), is thought to play an essential role in the oncogenesis of thyroid follicular tumors. To identify PPFP-targeted drug candidates and establish an early standard of care for thyroid tumors, we performed ensemble-docking-based compound screening. Specifically, we investigated the pocket structure that should be adopted to search for a promising ligand compound for the PPFP; the position of the ligand-binding pocket on the PPARγ side of the PPFP is similar to that of PPARγ; however, the shape is slightly different between them due to environmental factors. We developed a method for selecting a PPFP structure with a relevant pocket and high prediction accuracy for ligand binding. This method was validated using PPARγ, whose structure and activity values are known for many compounds. Then, we performed docking calculations to the PPFP for 97 drug or drug-like compounds registered in the DrugBank database with a thiazolidine backbone, which is one of the characteristics of ligands that bind well to PPARγ. Furthermore, the binding affinities of promising ligand candidates were estimated more reliably using the molecular mechanics Poisson–Boltzmann surface area method. Thus, we propose promising drug candidates for the PPFP with a thiazolidine backbone. [ABSTRACT FROM AUTHOR]

Structure‐based drug design is a widely applied approach in the discovery of new lead compounds for known therapeutic targets. In most structure‐based drug design applications, the docking procedure is considered the crucial step. Here, a potential ligand is fitted into the binding site, and a scoring function assesses its binding capability. With the rise of modern machine‐learning in drug discovery, novel scoring functions using machine‐learning techniques achieved significant performance gains in virtual screening and ligand optimization tasks on retrospective data. However, real‐world applications of these methods are still limited. Missing success stories in prospective applications are one reason for this. Additionally, the fast‐evolving nature of the field makes it challenging to assess the advantages of each individual method. This review will highlight recent strides toward improved real world applicability of machine‐learning based scoring, enabling a better understanding of the potential benefits and pitfalls of these functions on a project. Furthermore, a systematic way of classifying machine‐learning based scoring that facilitates comparisons will be presented. This article is categorized under:Data Science > ChemoinformaticsData Science > Artificial Intelligence/Machine LearningSoftware > Molecular Modeling [ABSTRACT FROM AUTHOR]

Here, we describe the identification of an antibiotic class acting via LpxH, a clinically unexploited target in lipopolysaccharide synthesis. The lipopolysaccharide synthesis pathway is essential in most Gram-negative bacteria and there is no analogous pathway in humans. Based on a series of phenotypic screens, we identified a hit targeting this pathway that had activity on efflux-defective strains of Escherichia coli. We recognized common structural elements between this hit and a previously published inhibitor, also with activity against efflux-deficient bacteria. With the help of X-ray structures, this information was used to design inhibitors with activity on efflux-proficient, wild-type strains. Optimization of properties such as solubility, metabolic stability and serum protein binding resulted in compounds having potent in vivo efficacy against bloodstream infections caused by the critical Gram-negative pathogens E. coli and Klebsiella pneumoniae. Other favorable properties of the series include a lack of pre-existing resistance in clinical isolates, and no loss of activity against strains expressing extended-spectrum-β-lactamase, metallo-β-lactamase, or carbapenemase-resistance genes. Further development of this class of antibiotics could make an important contribution to the ongoing struggle against antibiotic resistance. [ABSTRACT FROM AUTHOR]

Background: In the present work, molecular docking analysis was conducted on proposed protein derivatives from the protein data bank using the Auto Dock for Docking programme. study on protein binding affinity using 1-acetyl-3-(2-aminophenyl)-5,5-diphenylimidazolidine- 2,4-dione derivatives and the GABA receptor. Even if a number of innovative anticonvulsants have been created in India, some types of seizures are still not adequately controlled by smaller side effects even after the use of modern and revolutionary treatments. Materials and Methods: Target-oriented virtual screening of ligands under investigation with adaptable molecular docking techniques. We can choose the most promising compounds for further study of various derivatives by evaluating the ligands' affinity to the GABAA active sites. Via the inhibitor GABAergic route, a number of 5,5-diphenylimidazolidine derivatives were designed and investigated in silico. Results: Using the AutoDock 4.2 programme, a number of molecules associated with 5,5-diphenylimidazolidine were investigated for molecular docking, acute prediction, and ADMET analysis. According to docking research, these molecules have at least one hydrogen bond stabilising them. The proposed composites were all investigated for Gamma Aminobutyric Acid (GABA) inhibitory exertion and all shown good in silico ADME and molecular docking results. Conclusion: One of the essential enzymes for biosynthesis in many natural ecosystems, Gamma Aminobutyric Acid (GABA) is present in both humans and animals. By meticulously replicating the prior pharmacological experiment's circumstances, we are able to compare the outcomes and talk about certain commonalities in how molecule fragments affect anticonvulsant action. Docking score, ADMET analysis, acute toxicity prediction, and structural location of ligands in the active GABAA site enzyme were found to positively correlate, supporting the viability of target-based virtual screening as a way to expedite pharmacological screening. [ABSTRACT FROM AUTHOR]

G protein-coupled receptors (GPCRs) play a key role in the transduction of extracellular signals to cells and regulation of many biological processes, which makes these membrane proteins one of the most important targets for pharmacological agents. A significant increase in the number of resolved atomic structures of GPCRs has opened the possibility of developing pharmaceuticals targeting these receptors via structure-based drug design (SBDD). SBDD employs information on the structure of receptor–ligand complexes to search for selective ligands without the need for an extensive high-throughput experimental ligand screening and can significantly expand the chemical space for ligand search. In this review, we describe the process of deciphering GPCR structures using X-ray diffraction analysis and cryoelectron microscopy as an important stage in the rational design of drugs targeting this receptor class. Our main goal was to present modern developments and key features of experimental methods used in SBDD of GPCR-targeting agents to a wide range of specialists. [ABSTRACT FROM AUTHOR]

Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs. [ABSTRACT FROM AUTHOR]

The B‐cell lymphoma‐2 (BCL‐2) family of proteins plays a crucial role in the regulation of apoptosis, offering a dual mechanism for its control. Numerous studies have established a strong association between gene disorders of these proteins and the proliferation of diverse cancer cell types. Consequently, the identification and development of drugs targeting BCL‐2 family proteins have emerged as a prominent area in antitumor therapy. Over the last two decades, several small‐molecules have been designed to modulate the protein–protein interactions between anti‐ and proapoptotic BCL‐2 proteins, effectively suppressing tumor growth and metastasis in vivo. The primary focus of research has been on developing BCL‐2 homology 3 (BH3) mimetics to target antiapoptotic BCL‐2 proteins, thereby competitively releasing proapoptotic BCL‐2 proteins and restoring the blocked intrinsic apoptotic program. Additionally, for proapoptotic BCL‐2 proteins, exogenous small molecules have been explored to activate cell apoptosis by directly interacting with executioner proteins such as BCL‐2‐associated X protein (BAX) or BCL‐2 homologous antagonist/killer protein (BAK). In this comprehensive review, we summarize the inhibitors and activators (sensitizers) of BCL‐2 family proteins developed over the past decades, highlighting their discovery, optimization, preclinical and clinical status, and providing an overall landscape of drug development targeting these proteins for therapeutic purposes. [ABSTRACT FROM AUTHOR]

Abstract The prevalence of HIV-1 infection continues to pose a significant global public health issue, highlighting the need for antiretroviral drugs that target viral proteins to reduce viral replication. One such target is HIV-1 protease (PR), responsible for cleaving viral polyproteins, leading to the maturation of viral proteins. While darunavir (DRV) is a potent HIV-1 PR inhibitor, drug resistance can arise due to mutations in HIV-1 PR. To address this issue, we developed a novel approach using the fragment molecular orbital (FMO) method and structure-based drug design to create DRV analogs. Using combinatorial programming, we generated novel analogs freely accessible via an on-the-cloud mode implemented in Google Colab, Combined Analog generator Tool (CAT). The designed analogs underwent cascade screening through molecular docking with HIV-1 PR wild-type and major mutations at the active site. Molecular dynamics (MD) simulations confirmed the assess ligand binding and susceptibility of screened designed analogs. Our findings indicate that the three designed analogs guided by FMO, 19–0–14–3, 19–8–10–0, and 19–8–14–3, are superior to DRV and have the potential to serve as efficient PR inhibitors. These findings demonstrate the effectiveness of our approach and its potential to be used in further studies for developing new antiretroviral drugs.

As a novel and promising antitumor target, AXL plays an important role in tumor growth, metastasis, immunosuppression and drug resistance of various malignancies, which has attracted extensive research interest in recent years. In this study, by employing the structure-based drug design and bioisosterism strategies, we designed and synthesized in total 54 novel AXL inhibitors featuring a fused-pyrazolone carboxamide scaffold, of which up to 20 compounds exhibited excellent AXL kinase and BaF3/TEL-AXL cell viability inhibitions. Notably, compound 59 showed a desirable AXL kinase inhibitory activity (IC50: 3.5 nmol/L) as well as good kinase selectivity, and it effectively blocked the cellular AXL signaling. In turn, compound 59 could potently inhibit BaF3/TEL-AXL cell viability (IC50: 1.5 nmol/L) and significantly suppress GAS6/AXL-mediated cancer cell invasion, migration and wound healing at the nanomolar level. More importantly, compound 59 oral administration showed good pharmacokinetic profile and in vivo antitumor efficiency, in which we observed significant AXL phosphorylation suppression, and its antitumor efficacy at 20 mg/kg (qd) was comparable to that of BGB324 at 50 mg/kg (bid), the most advanced AXL inhibitor. Taken together, this work provided a valuable lead compound as a potential AXL inhibitor for the further antitumor drug development.

Over the years, the field of GPCR drug design has undergone a remarkable evolution, fueled by advancements in science and technology. This evolution has given rise to a diverse range of ideas and approaches in structure-based drug design, bolstering the versatility and strength of the GPCR drug design toolbox. This review encapsulates the iterative development process, navigating challenges and opportunities in structure-based drug design within GPCRs. With a focused emphasis on its impact on psychiatric disorders, the review accentuates recent advancements and delves into the potentials unlocked by emerging technologies. The review explores the intricate interplay between scientific progress and iterative refinement, offering profound insights into the potential pathways that lie ahead for GPCR drug design. Cao et al. outline the iterative development, challenges, and opportunities in structure-based drug design (SBDD) for GPCRs, emphasizing the impact of SBDD on translational research for psychiatric disorders and offering insights for future GPCR drug design. [ABSTRACT FROM AUTHOR]

Aberrant brain‐derived neurotrophic factor (BDNF) signaling has been proposed to contribute to the pathophysiology of depression and other neurological disorders such as Angelman syndrome. We have previously shown that targeting the tropomyosin receptor kinase B/postsynaptic density protein‐95 (PSD‐95) nexus in the BDNF signaling pathway by peptidomimetic inhibitors is a promising approach for therapeutic intervention. Here, we used structure‐based knowledge to develop a new Syn3 peptidomimetic compound series that fuses peptides derived from the PSD‐95‐binding protein SynGAP to our prototype compound CN2097. The new compounds target the PSD‐95 PDZ3 domain and adjoining αC helix to achieve bivalent binding that results in up to 7‐fold stronger affinity compared to CN2097. These compounds were designed to improve CN2097 specificity for the PSD‐95 PDZ3 domain, and structure–activity relationship studies were performed to improve their resistance to proteolysis. [ABSTRACT FROM AUTHOR]

The salt-inducible kinases (SIK) 1-3 are key regulators of pro- versus anti-inflammatory cytokine responses during innate immune activation. The lack of highly SIK-family or SIK isoform-selective inhibitors suitable for repeat, oral dosing has limited the study of the optimal SIK isoform selectivity profile for suppressing inflammation in vivo. To overcome this challenge, we devised a structure-based design strategy for developing potent SIK inhibitors that are highly selective against other kinases by engaging two differentiating features of the SIK catalytic site. This effort resulted in SIK1/2-selective probes that inhibit key intracellular proximal signaling events including reducing phosphorylation of the SIK substrate cAMP response element binding protein (CREB) regulated transcription coactivator 3 (CRTC3) as detected with an internally generated phospho-Ser329-CRTC3-specific antibody. These inhibitors also suppress production of pro-inflammatory cytokines while inducing anti-inflammatory interleukin-10 in activated human and murine myeloid cells and in mice following a lipopolysaccharide challenge. Oral dosing of these compounds ameliorates disease in a murine colitis model. These findings define an approach to generate highly selective SIK1/2 inhibitors and establish that targeting these isoforms may be a useful strategy to suppress pathological inflammation. [ABSTRACT FROM AUTHOR]

Determination of the bound pose of a ligand is a critical first step in many in silico drug discovery tasks. Molecular docking is the main tool for the prediction of non-covalent binding of a protein and ligand system. Molecular docking pipelines often only utilize the information of one ligand binding to the protein despite the commonly held hypothesis that different ligands share binding interactions when bound to the same receptor. Here we describe Open-ComBind, an easy-to-use, open-source version of the ComBind molecular docking pipeline that leverages information from multiple ligands without known bound structures to enhance pose selection. We first create distributions of feature similarities between ligand pose pairs, comparing near-native poses with all sampled docked poses. These distributions capture the likelihood of observing similar features, such as hydrogen bonds or hydrophobic contacts, in different pose configurations. These similarity distributions are then combined with a per-ligand docking score to enhance overall pose selection by 5% and 4.5% for high-affinity and congeneric series helper ligands, respectively. Open-ComBind reduces the average RMSD of ligands in our benchmark dataset by 9.0%. We provide Open-ComBind as an easy-to-use command line and Python API to increase pose prediction performance at . [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) virus syndrome caused the recent outbreak of COVID-19 disease, the most significant challenge to public health for decades. Despite the successful development of vaccines and promising therapies, the development of novel drugs is still in the interests of scientific society. SARS-CoV-2 main protease Mpro is one of the key proteins for the lifecycle of the virus and is considered an intriguing target. We used a structure-based drug design approach as a part of the search of new inhibitors for SARS-CoV-2 Mpro and hence new potential drugs for treating COVID-19. Four structures of potential inhibitors of (4S)-2-(2-(1H-imidazol-5-yl)ethyl)-4-amino-2-(1,3-dihydroxypropyl)-3-hydroxy-5-(1H-imidazol-5-yl)pentanal (L1), (2R,4S)-2-((1H-imidazol-4-yl)methyl)-4-chloro-8-hydroxy-7-(hydroxymethyl)octanoic acid (L2), 1,9-dihydroxy-6-(hydroxymethyl)-6-(((1S)-1,7,7-trimethylbicyclo [2.2.1]heptan-2-yl)amino)nonan-4-one (L3), and 2,4,6-tris((4H-1,2,4-triazol-3-yl)amino)benzonitrile (L4) were modeled. Three-dimensional structures of ligand–protein complexes were modeled and their potential binding efficiency proved. Docking and molecular dynamic simulations were performed for these compounds. Detailed trajectory analysis of the ligands' binding conformation was carried out. Binding free energies were estimated by the MM/PBSA approach. Results suggest a high potential efficiency of the studied inhibitors. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 caused the ongoing COVID-19 pandemic, and only a few treatment options are available to mitigate its impact on human health. Hence, there is a need to discover drugs that could be used to treat COVID-19. Several studies have already reported the repurposing of existing drugs to inhibit the receptor-binding domain of SARS-CoV-2. However, the emergence of COVID-19 variants may render current drug candidates ineffective. Here, we report the structure-based drug screening of the DrugBank database against the wild-type, B.1.1.7, B.1.351, and P.1 variants of SARS-CoV-2. Our study revealed that Salmeterol, Abediterol, and Lysophosphatidylglycerol are among the top candidates against all four variants. Furthermore, we showed that Salmeterol forms a stable complex with the receptor binding domain of SARS-CoV-2 variants. Further studies are needed to evaluate the clinical relevance of the drug candidates discovered. Nevertheless, this study provides insight into computational drug design that works against multiple variants of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

The worldwide emergence and dissemination of Gram-negative bacteria expressing metallo-β-lactamases (MBLs) menace the efficacy of all β-lactam antibiotics, including carbapenems, a last-line treatment usually restricted to severe pneumonia and urinary tract infections. Nonetheless, no MBL inhibitor is yet available in therapy. We previously identified a series of 1,2,4-triazole-3-thione derivatives acting as micromolar inhibitors of MBLs in vitro, but devoid of synergistic activity in microbiological assays. Here, via a multidisciplinary approach, including molecular modelling, synthesis, enzymology, microbiology, and X-ray crystallography, we optimized this series of compounds and identified low micromolar inhibitors active against clinically relevant MBLs (NDM-1- and VIM-type). The best inhibitors increased, to a certain extent, the susceptibility of NDM-1- and VIM-4-producing clinical isolates to meropenem. X-ray structures of three selected inhibitors in complex with NDM-1 elucidated molecular recognition at the base of potency improvement, confirmed in silico predicted orientation, and will guide further development steps. [ABSTRACT FROM AUTHOR]

As a novel and promising antitumor target, AXL plays an important role in tumor growth, metastasis, immunosuppression and drug resistance of various malignancies, which has attracted extensive research interest in recent years. In this study, by employing the structure-based drug design and bioisosterism strategies, we designed and synthesized in total 54 novel AXL inhibitors featuring a fused-pyrazolone carboxamide scaffold, of which up to 20 compounds exhibited excellent AXL kinase and BaF3/TEL-AXL cell viability inhibitions. Notably, compound 59 showed a desirable AXL kinase inhibitory activity (IC 50 : 3.5 nmol/L) as well as good kinase selectivity, and it effectively blocked the cellular AXL signaling. In turn, compound 59 could potently inhibit BaF3/TEL-AXL cell viability (IC 50 : 1.5 nmol/L) and significantly suppress GAS6/AXL-mediated cancer cell invasion, migration and wound healing at the nanomolar level. More importantly, compound 59 oral administration showed good pharmacokinetic profile and in vivo antitumor efficiency, in which we observed significant AXL phosphorylation suppression, and its antitumor efficacy at 20 mg/kg (qd) was comparable to that of BGB324 at 50 mg/kg (bid), the most advanced AXL inhibitor. Taken together, this work provided a valuable lead compound as a potential AXL inhibitor for the further antitumor drug development. A class of novel AXL inhibitors has been discovered, in which compound 59 exhibited potent AXL inhibition, high kinase selectivity and significant in vivo anti-tumor efficacy with desirable pharmacokinetic profile. [Display omitted] [ABSTRACT FROM AUTHOR]

The type-II collagenase, also known as matrix metalloproteinase-8 (MMP-8) plays a vital role in a variety of physiological illnesses namely cardiovascular, musculoskeletal, renal, digestive, and respiratory ailments including cancer. However, numerous novel compounds with effective MMP-8 inhibitory activity have been created by research and academic institutions, pharmaceutical companies, and both. Sadly, despite showing promising results, these compounds have a variety of negative side effects. In this context, a number of MMP-8 inhibitors having potential inhibitory activity with different key structural features such as hydroxamates, carboxylic acid, thiol and mercaptosulfide, phosphonate, and pyrimidine-2,4,6-trione have been discussed. Additionally, various ligand-based and structure-based drug designs that have been previously conducted on a variety of MMP-8 inhibitors have been thoroughly reviewed and that can offer important information regarding the development of novel MMP-8 inhibitors in the future. Therefore, the detailed knowledge about the key structural features of the existing MMP-8 inhibitors can be found, and further with the help of drug design strategies, a novel class of collagenase inhibitors may be identified. In light of this, this work may be beneficial for researchers looking into the processes involved in developing and identifying new potential and promising MMP-8 inhibitors.

Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.

Clostridium histolyticum is a Gram-positive anaerobic bacterium belonging to the Clostridium genus. It produces collagenase, an enzyme involved in breaking down collagen which is a key component of connective tissues. However, antimicrobial resistance (AMR) poses a great challenge in combating infections caused by this bacteria. The lengthy nature of traditional drug development techniques has resulted in a shift to computer-aided drug design and other modern drug discovery approaches. The above method offers a cost-effective means for gathering comprehensive information about how ligands interact with their target proteins. The objective of this study is to create novel, explicit drugs that specifically inhibit the C. histolyticum collagenase enzyme. Through structure-based virtual screening, a library containing 1830 compounds was screened to identify potential drug candidates against collagenase enzymes. Following that, molecular dynamic (MD) simulation was performed in an aqueous solution to evaluate the behavior of protein and ligand in a dynamic environment while density functional theory (DFT) analysis was executed to predict the molecular properties and structure of lead compounds, and the WaterSwap technique was utilized to obtain insights into the drug–protein interaction with water molecules. Furthermore, principal component analysis (PCA) was performed to reveal conformational changes, salt bridges to express electrostatic interaction and protein stability, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) to assess the pharmacokinetics profile of top compounds and control molecules. Three potent drug candidates were identified MSID000001, MSID000002, MSID000003, and the control with a binding score of −10.7 kcal/mol, −9.8 kcal/mol, −9.5 kcal/mol, and −8 kcal/mol, respectively. Furthermore, Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) analysis of the simulation trajectories revealed energy scores of −79.54 kcal/mol, −73.99 kcal/mol, −62.26 kcal/mol, and −70.66 kcal/mol, correspondingly. The pharmacokinetics properties exhibited were under the acceptable range. The compounds hold the potential to be novel drugs; therefore, further investigation needs to be conducted to find out their anti-collagenase action against C. histolyticum infections and antibiotic resistance.

Vascular Endothelial Growth Factor Receptor 1 (VEGFR1), is an enzyme with tyrosine kinase activity that plays a pivotal role in angiogenesis, the process of new blood vessel formation. This receptor is of significant clinical importance as it is implicated in various cancers, particularly non-small cell lung cancer (NSCLC), where its dysregulation leads to uncontrolled cell growth through ligand-induced phosphorylation. While commercially available drugs target VEGFR1, their prolonged use often leads to drug resistance and the emergence of mutations in cancer patients. To address these challenges, researchers have identified the human tyrosine kinase (hTK) domain of VEGFR1 as a potential therapeutic marker for lung malignancies. The 3D crystal structure of the hTK domain, obtained from Protein Data Bank (PDB ID: 3HNG), has provided vital structural insights of hVEGFR1. This study has revealed variations within the hVEGFR1 tyrosine kinase domain, distinguishing between regions associated with phosphorylase kinase and transferase activities. We identified numerous potential phosphorylation sites within the TK domain, shedding light on the protein's regulation and signaling possible. Detailed molecular interaction analyses have elucidated the binding forces between lead molecules and hVEGFR1, including hydrogen bonds, electrostatic, hydrophobic, and π-sigma interactions. The stability observed during molecular dynamics simulations further underscores the biological relevance of these interactions. Furthermore, docked complexes has highlighted localized structural fluctuations, offering insight into potential allosteric effects and dynamic conformational changes induced by lead molecules. These findings not only provide a comprehensive characterization of hVEGFR1 but also pave the way for the development of targeted therapies. Eventually, this study has the potential in identifying drug to combat diseases associated with hVEGFR1 dysregulation, including cancer and angiogenesis-related disorders, contributing to effective treatment strategies.

Introduction:Helicobacter pylori is a bacterium that colonizes the gastric epithelium, which affects millions of people worldwide. H. pylori infection can lead to various gastrointestinal diseases, including gastric adenocarcinoma and mucosa-associated lymphoid tissue lymphoma. Conventional antibiotic therapies face challenges due to increasing antibiotic resistance and patient non-compliance, necessitating the exploration of alternative treatment approaches. In this study, we focused on Hp0231 (DsbK), an essential component of the H. pylori Dsb (disulfide bond) oxidative pathway, and investigated peptide-based inhibition as a potential therapeutic strategy.Methods: Three inhibitory peptides designed by computational modeling were evaluated for their effectiveness using a time-resolved fluorescence assay. We also examined the binding affinity between Hp0231 and the peptides using microscale thermophoresis.Results and discussion: Our findings demonstrate that in silico-designed synthetic peptides can effectively inhibit Hp0231-mediated peptide oxidation. Targeting Hp0231 oxidase activity could attenuate H. pylori virulence without compromising bacterial viability. Therefore, peptide-based inhibitors of Hp0231 could be candidates for the development of new targeted strategy, which does not influence the composition of the natural human microbiome, but deprive the bacterium of its pathogenic properties.

Simple Summary: Innovative approaches are required to combat the complexity and adaptability of cancerous cells. Proteins that bind to DNA play an important role in the ability of cancerous cells to survive traditional treatments. This study explores small molecules that bind to one specific protein in these mechanisms—human single-stranded DNA binding protein 1. Using complementary computational and experimental approaches, we discovered three small molecules that appear to prevent the protein from binding to DNA. The computational tools suggest how the compounds bind to human single-stranded DNA binding protein 1, and cellular studies indicate that the molecules may interfere with the cell's ability to repair DNA at certain concentrations. Further work is necessary to understand how these compounds interact with cells, and to develop them into selective hSSB1 inhibitors. Human single-stranded DNA binding protein 1 (hSSB1) is critical to preserving genome stability, interacting with single-stranded DNA (ssDNA) through an oligonucleotide/oligosaccharide binding-fold. The depletion of hSSB1 in cell-line models leads to aberrant DNA repair and increased sensitivity to irradiation. hSSB1 is over-expressed in several types of cancers, suggesting that hSSB1 could be a novel therapeutic target in malignant disease. hSSB1 binding studies have focused on DNA; however, despite the availability of 3D structures, small molecules targeting hSSB1 have not been explored. Quinoline derivatives targeting hSSB1 were designed through a virtual fragment-based screening process, synthesizing them using AlphaLISA and EMSA to determine their affinity for hSSB1. In parallel, we further screened a structurally diverse compound library against hSSB1 using the same biochemical assays. Three compounds with nanomolar affinity for hSSB1 were identified, exhibiting cytotoxicity in an osteosarcoma cell line. To our knowledge, this is the first study to identify small molecules that modulate hSSB1 activity. Molecular dynamics simulations indicated that three of the compounds that were tested bound to the ssDNA-binding site of hSSB1, providing a framework for the further elucidation of inhibition mechanisms. These data suggest that small molecules can disrupt the interaction between hSSB1 and ssDNA, and may also affect the ability of cells to repair DNA damage. This test study of small molecules holds the potential to provide insights into fundamental biochemical questions regarding the OB-fold. [ABSTRACT FROM AUTHOR]

Despite essential experimental efforts focused on studying severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2), computational chemistry methods are promising complementary tools in combating coronavirus disease 2019 (COVID-19). The present review aims to provide readers with the recent progress and advances in computational approaches currently used to streamline drug discovery and development in the context of COVID-19. Our review is dual purpose. It is intended (a) to familiarize the readership with the general concept of QSAR, in silico screening, molecular docking and molecular dynamics (MD) simulations and (b) to provide key examples of the recent applications of these computational tools in discovering novel therapeutic agents against COVID-19. We outline how structure- and ligand-based drug design can accelerate the structural elucidation of pharmacological drug targeting and the discovery of preclinical drug candidate molecules. Several examples of MD computational studies demonstrate how atomistic MD simulations can facilitate our understanding of the molecular basis of drug actions and biological mechanisms of virus inhibition in atomic detail. Finally, the short- and long-term perspectives in computational drug discovery are discussed. The aim of this study is to summarize the last three years' progress and advances in computational approaches currently used to streamline the drug discovery and development process in the context of COVID-19. Materials and methods. The literature overview of QSAR, in silico screening, machine learning, molecular docking and molecular dynamics (MD) simulations is given in the context of COVID-19. The literature search was performed using online databases, such as Scopus, Web of Science, PDB-protein databank, and PubMed, focusing on the following keywords - human coronavirus, QSAR, molecular docking, virtual screening, machine learning, molecular dynamics, Mpro and PLpro proteases, SARS-CоV-2, respectively. Results. The review familiarizes the readership with the general concept of QSAR, in silico screening, machine learning, molecular docking and MD simulations and provides key examples of the recent applications of these computational tools in discovering novel therapeutic agents against COVID-19. Conclusions. New insight into the recent progress and achievements in computer-guided drug discovery for therapeutic agents against SARS-CoV-2 is provided. [ABSTRACT FROM AUTHOR]

Serial crystallography has emerged as an important tool for structural studies of integral membrane proteins. The ability to collect data from micrometre-sized weakly diffracting crystals at room temperature with minimal radiation damage has opened many new opportunities in time-resolved studies and drug discovery. However, the production of integral membrane protein microcrystals in lipidic cubic phase at the desired crystal density and quantity is challenging. This paper introduces VIALS (versatile approach to high-density microcrystals in lipidic cubic phase for serial crystallography), a simple, fast and efficient method for preparing hundreds of microlitres of high-density microcrystals suitable for serial X-ray diffraction experiments at both synchrotron and free-electron laser sources. The method is also of great benefit for rational structure-based drug design as it facilitates in situ crystal soaking and rapid determination of many cocrystal structures. Using the VIALS approach, room-temperature structures are reported of (i) the archaerhodopsin-3 protein in its dark-adapted state and 110 ns photocycle intermediate, determined to 2.2 and 1.7 Å, respectively, and (ii) the human A2A adenosine receptor in complex with two different ligands determined to a resolution of 3.5 Å. [ABSTRACT FROM AUTHOR]

Protein-protein interactions (PPIs) aSre pivotal in biology and present promising therapeutic opportunities. This review focuses on recent strides in developing small molecule PPI inhibitors, emphasizing their structural insights, mechanisms, and clinical applications. Small molecules possess distinct advantages, including oral administration convenience, cell penetration, chemical diversity, and cost-effectiveness, making them vital in drug discovery. Methods to target PPIs span high-throughput screening, structure-based design, fragment-based discovery, computational tools, and chemical biology techniques. Case studies illustrate successful inhibitors, spotlighting specific targets and mechanisms. Innovative screening platforms like phenotypic screening, covalent inhibitors, PROTACs and molecular glues are examined alongside structural revelations enabled by Cryo-electron microscopy and X-ray crystallography. Elevating selectivity in small molecule PPI inhibitors remains pivotal. Challenges, including druggability, toxicity, resistance and regulatory intricacies are acknowledged. The future landscape encompasses target identification, systems biology integration, personalized medicine, and therapeutic potential in traditionally elusive targets. The review also traverses clinical applications and the PPI inhibitor pipeline, including compounds in trials and approved drugs. Safety assessments, comprising predictive models and pharmacokinetics/pharmacodynamics studies, ensure the secure and efficient development of PPI inhibitors. In summary, this review offers a comprehensive exploration of small molecule inhibitors for PPIs in drug discovery, underlining their transformative potential, persisting challenges, and auspicious applications across diverse disease contexts. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug–target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts. [ABSTRACT FROM AUTHOR]

Due to the structural complexity of proteins, their corresponding crystal arrangements generally contain a significant amount of solvent‐occupied space. These areas allow a certain degree of intracrystalline protein flexibility and mobility of solutes. Therefore, knowledge of the geometry of solvent‐filled channels and cavities is essential whenever the dynamics inside a crystal are of interest. Especially in soaking experiments for structure‐based drug design, ligands must be able to traverse the crystal solvent channels and reach the corresponding binding pockets. Unsuccessful screenings are sometimes attributed to the geometry of the crystal packing, but the underlying causes are often difficult to understand. This work presents LifeSoaks, a novel tool for analyzing and visualizing solvent channels in protein crystals. LifeSoaks uses a Voronoi diagram‐based periodic channel representation which can be efficiently computed. The size and location of channel bottlenecks, which might hinder molecular diffusion, can be directly derived from this representation. This work presents the calculated bottleneck radii for all crystal structures in the PDB and the analysis of a new, hand‐curated data set of structures obtained by soaking experiments. The results indicate that the consideration of bottleneck radii and the visual inspection of channels are beneficial for planning soaking experiments. [ABSTRACT FROM AUTHOR]

This paper describes the structure-based design of a preliminary drug candidate against COVID-19 using free software and publicly available X-ray crystallographic structures. The goal of this tutorial is to disseminate skills in structure-based drug design and to allow others to unleash their own creativity to design new drugs to fight the current pandemic. The tutorial begins with the X-ray crystallographic structure of the main protease (Mpro) of the SARS coronavirus (SARS-CoV) bound to a peptide substrate and then uses the UCSF Chimera software to modify the substrate to create a cyclic peptide inhibitor within the Mpro active site. Finally, the tutorial uses the molecular docking software AutoDock Vina to show the interaction of the cyclic peptide inhibitor with both SARS-CoV Mpro and the highly homologous SARS-CoV-2 Mpro. The supporting information provides an illustrated step-by-step protocol, as well as a video showing the inhibitor design process, to help readers design their own drug candidates for COVID-19 and the coronaviruses that will cause future pandemics. An accompanying preprint in bioRxiv [https://doi.org/10.1101/2020.08.03.234872] describes the synthesis of the cyclic peptide and the experimental validation as an inhibitor of SARS-CoV-2 Mpro.

Abstract Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time- and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy (cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence (AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of medium-resolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.

The PAX8/PPARγ rearrangement, producing the PAX8–PPARγ fusion protein (PPFP), is thought to play an essential role in the oncogenesis of thyroid follicular tumors. To identify PPFP-targeted drug candidates and establish an early standard of care for thyroid tumors, we performed ensemble-docking-based compound screening. Specifically, we investigated the pocket structure that should be adopted to search for a promising ligand compound for the PPFP; the position of the ligand-binding pocket on the PPARγ side of the PPFP is similar to that of PPARγ; however, the shape is slightly different between them due to environmental factors. We developed a method for selecting a PPFP structure with a relevant pocket and high prediction accuracy for ligand binding. This method was validated using PPARγ, whose structure and activity values are known for many compounds. Then, we performed docking calculations to the PPFP for 97 drug or drug-like compounds registered in the DrugBank database with a thiazolidine backbone, which is one of the characteristics of ligands that bind well to PPARγ. Furthermore, the binding affinities of promising ligand candidates were estimated more reliably using the molecular mechanics Poisson–Boltzmann surface area method. Thus, we propose promising drug candidates for the PPFP with a thiazolidine backbone.

We report here the discovery and structure-guided optimization of a novel series of AKT kinase inhibitors. Based on docking studies for the predicted active bound-conformation of 2, a potent series of N-substituted-5-(isoquinolin-6-yl)-1,3,4-thiadiazol-2-amines was developed. Compounds in the series achieve AKT pathway inhibition in cancer cells, as measured by inhibition of pathway proteins pGSK and pFKHR. Compound 12 was further evaluated in a single dose PK/PD in vivo study in tumor-bearing mice and demonstrated inhibition of phosphorylation of the direct substrate GSK and pathway biomarker S6. [ABSTRACT FROM AUTHOR]

With the significant growth of patients suffering from neurodegenerative diseases (NDs), novel classes of compounds targeting monoamine oxidase type B (MAO-B) are promptly emerging as distinguished structures for the treatment of the latter. As a promising function of computer-aided drug design (CADD), structure-based virtual screening (SBVS) is being heavily applied in processes of drug discovery and development. The utilization of molecular docking, as a helping tool for SBVS, is providing essential data about the poses and the occurring interactions between ligands and target molecules. The current work presents a brief discussion of the role of MAOs in the treatment of NDs, insight into the advantages and drawbacks of docking simulations and docking software, and a look into the active sites of MAO-A and MAO-B and their main characteristics. Thereafter, we report new chemical classes of MAO-B inhibitors and the essential fragments required for stable interactions focusing mainly on papers published in the last five years. The reviewed cases are separated into several chemically distinct groups. Moreover, a modest table for rapid revision of the revised works including the structures of the reported inhibitors together with the utilized docking software and the PDB codes of the crystal targets applied in each study is provided. Our work could be beneficial for further investigations in the search for novel, effective, and selective MAO-B inhibitors. [ABSTRACT FROM AUTHOR]

Protein–protein interactions (PPIs) have vital roles in almost all cellular processes, although they are regarded as 'undruggable' therapeutic targets because of their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) shows advantages for the design of modulators to inhibit or stabilize PPIs, with two drugs approved and more than ten compounds in clinical trials. Fragments tend to bind at 'hot spots' of PPI interfaces, and the locations of different 'hot spots' provide directions for fragment evolution to discover more potent PPI modulators. Fragment hits with new mode of actions, including covalent fragments and fragments binding at allosteric sites, have become important sources of PPI modulators. Emerging technologies, such as cryo-electron microscopy, covalent tethering, and artificial intelligence, are accelerating the application of FBDD targeting PPIs. Protein–protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs. [ABSTRACT FROM AUTHOR]

Objective: To develop Favipiravir, based predictive models of coronavirus disease 2019 (COVID-19) from small molecule databases such as PubChem, Drug Bank, Zinc Database, and literature. Methods: High Throughput Virtual Screening (HTVS) using different computational screening methods is used to identify the target and lead molecules. CoMFA (Comparative Molecular Field Analysis) is a 3D-QSAR procedure depending on information from known dynamic atoms and eventually permits one to plan and anticipate exercises of particles. These two analysis is used to train predictive models. Results: The predictive model achieved the highest accuracy score with a relatively small dataset size can be a subject of overfitting. Datasets with over 500 samples demonstrate an accuracy of about 85–95%, that can be considered as very good. Conclusions: From the result it is observed that Increasing level of potassium, sodium and nitrogen will lead to burst lipid bilayer membrane of virus which cause RNA replication rapidly. However, low level of sodium, potassium and nitrogen will help in the DNA polymerase inhibition and replication can be stopped. The best developed QSAR model in terms of the druggability and activity relation has been selected over the parent Favipiravir molecule for designing COVID-19 drugs may lead towards pharmaceutical development in future. [ABSTRACT FROM AUTHOR]

C3d is a hallmark protein of the complement system, whose presence is critical to measure the progression of several immune diseases. Here, we propose to directly target C3d through small peptides mimicking the binding of its natural ligand, the complement regulator Factor H (FH). Through iterative computational analysis and binding affinity experiments, we establish a rationale for the structure-based design of FH-inspired peptides, leading to low-micromolar affinity for C3d and stable binding over microsecond-length simulations. Our FH-inspired peptides call now for further optimization toward high-affinity binding and suggest that small peptides are promising as novel C3d biomarkers and therapeutic tools.

Abstract Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a tenacious pathogen that has latently infected one third of the world’s population. However, conventional TB treatment regimens are no longer sufficient to tackle the growing threat of drug resistance, stimulating the development of innovative anti-tuberculosis agents, with special emphasis on new protein targets. The Mtb genome encodes ~4000 predicted proteins, among which many enzymes participate in various cellular metabolisms. For example, more than 200 proteins are involved in fatty acid biosynthesis, which assists in the construction of the cell envelope, and is closely related to the pathogenesis and resistance of mycobacteria. Here we review several essential enzymes responsible for fatty acid and nucleotide biosynthesis, cellular metabolism of lipids or amino acids, energy utilization, and metal uptake. These include InhA, MmpL3, MmaA4, PcaA, CmaA1, CmaA2, isocitrate lyases (ICLs), pantothenate synthase (PS), Lysine-ε amino transferase (LAT), LeuD, IdeR, KatG, Rv1098c, and PyrG. In addition, we summarize the role of the transcriptional regulator PhoP which may regulate the expression of more than 110 genes, and the essential biosynthesis enzyme glutamine synthetase (GlnA1). All these enzymes are either validated drug targets or promising target candidates, with drugs targeting ICLs and LAT expected to solve the problem of persistent TB infection. To better understand how anti-tuberculosis drugs act on these proteins, their structures and the structure-based drug/inhibitor designs are discussed. Overall, this investigation should provide guidance and support for current and future pharmaceutical development efforts against mycobacterial pathogenesis.

The haem enzyme indoleamine 2,3-dioxygenase 1 (IDO1) catalyses the rate-limiting step in the kynurenine pathway of tryptophan metabolism and plays an essential role in immunity, neuronal function, and ageing. Expression of IDO1 in cancer cells results in the suppression of an immune response, and therefore IDO1 inhibitors have been developed for use in anti-cancer immunotherapy. Here, we report an extension of our previously described highly efficient haem-binding 1,2,3-triazole and 1,2,4-triazole inhibitor series, the best compound having both enzymatic and cellular IC50 values of 34 nM. We provide enzymatic inhibition data for almost 100 new compounds and X-ray diffraction data for one compound in complex with IDO1. Structural and computational studies explain the dramatic drop in activity upon extension to pocket B, which has been observed in diverse haem-binding inhibitor scaffolds. Our data provides important insights for future IDO1 inhibitor design.