Homology modeling in drug discovery: Overview, current applications, and future perspectives

Mechanistic and structural insights into vitamin B2 metabolizing enzyme riboflavin kinase from Leishmania donovani

Leishmania donovani relies on specific vitamins and cofactors crucial for its survival and pathogenesis. Tailoring therapies to disrupt these pathways offers a promising strategy for the treatment of Visceral Leishmaniasis. Current treatment regimens are limited due to drug resistance and high costs. The dependency of Leishmania parasites on Vitamin B2 and its metabolic products is not known. In this study, we have biochemically and biophysically characterized a Vitamin B2 metabolism enzyme, riboflavin kinase from L. donovani (LdRFK) which converts riboflavin (vitamin B2) into flavin mononucleotide (FMN). Sequence comparison with human counterpart reflects 31.58 % identity only, thus opening up the possibility of exploring it as drug target. The rfk gene was cloned, expressed and the recombinant protein was purified. Kinetic parameters of LdRFK were evaluated with riboflavin and ATP as substrates which showed differential binding affinity when compared with the human RFK enzyme. Thermal and denaturant stability of the enzyme was evaluated. The rfk gene was overexpressed in the parasites and its role in growth and cell cycle was evaluated. In the absence of crystal structure, homology modelling and molecular dynamic simulation studies were performed to predict LdRFK structure. The data shows differences in substrate binding between human and parasite enzyme. This raises the possibility of exploring LdRFK for specific designing of antileishmanial molecules. Gene disruption studies can further validate its candidature as antileishmanial target.

Computational studies of tyrosinase inhibitors

Computational studies have significantly advanced the understanding of tyrosinase (TYR) function, mechanism, and inhibition, accelerating the development of more effective and selective inhibitors. This chapter provides an overview of in silico studies on TYR inhibitors, emphasizing key inhibitory chemotypes and the main residues involved in ligand-target interactions. The chapter discusses tools applied in the context of TYR inhibitor development, e.g., structure-based virtual screening, molecular docking, artificial intelligence, and machine learning algorithms.

Antimicrobial activity of compounds identified by artificial intelligence discovery engine targeting enzymes involved in Neisseria gonorrhoeae peptidoglycan metabolism

BACKGROUND

Neisseria gonorrhoeae (Ng) causes the sexually transmitted disease gonorrhoea. There are no vaccines and infections are treated principally with antibiotics. However, gonococci rapidly develop resistance to every antibiotic class used and there is a need for developing new antimicrobial treatments. In this study we focused on two gonococcal enzymes as potential antimicrobial targets, namely the serine protease L,D-carboxypeptidase LdcA (NgO1274/NEIS1546) and the lytic transglycosylase LtgD (NgO0626/NEIS1212). To identify compounds that could interact with these enzymes as potential antimicrobials, we used the AtomNet virtual high-throughput screening technology. We then did a computational modelling study to examine the interactions of the most bioactive compounds with their target enzymes. The identified compounds were tested against gonococci to determine minimum inhibitory and bactericidal concentrations (MIC/MBC), specificity, and compound toxicity in vitro.

RESULTS

AtomNet identified 74 compounds that could potentially interact with Ng-LdcA and 84 compounds that could potentially interact with Ng-LtgD. Through MIC and MBC assays, we selected the three best performing compounds for both enzymes. Compound 16 was the most active against Ng-LdcA, with a MIC50 value < 1.56 µM and MBC50/90 values between 0.195 and 0.39 µM. In general, the Ng-LdcA compounds showed higher activity than the compounds directed against Ng-LtgD, of which compound 45 had MIC50 values of 1.56-3.125 µM and MBC50/90 values between 3.125 and 6.25 µM. The compounds were specific for gonococci and did not kill other bacteria. They were also non-toxic for human conjunctival epithelial cells as judged by a resazurin assay. To support our biological data, in-depth computational modelling study detailed the interactions of the compounds with their target enzymes. Protein models were generated in silico and validated, the active binding sites and amino acids involved elucidated, and the interactions of the compounds interacting with the enzymes visualised through molecular docking and Molecular Dynamics Simulations for 50 ns and Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA).

CONCLUSIONS

We have identified bioactive compounds that appear to target the N. gonorrhoeae LdcA and LtgD enzymes. By using a reductionist approach involving biological and computational data, we propose that compound Ng-LdcA-16 and Ng-LtgD-45 are promising anti-gonococcal compounds for further development.

Advancing rational pesticide development against Drosophila suzukii: bioinformatics tools and applications-a systematic review

CONTEXT

Drosophila suzukii (Matsumura, 1931) is a widespread agricultural pest responsible for significant damage to various soft-skinned fruit hosts. The revolutionary potential of bioinformatics in agriculture emerges from its ability to provide extensive information on pests, fungi, chemical resistance, implications of non-target species, and other critical aspects. This wealth of information allows researchers to engage in projects and applied research in diverse agricultural domains that face these challenges. In this context, bioinformatics tools play a fundamental role. The negative impact of pests on crops, resulting in substantial economic losses, has highlighted the importance of in silico methods.

METHODS

To achieve this, we conducted a systematic search in scientific databases using as keywords "Drosophila suzukii," "biopesticides," "simulations computational," and "in-silico." After applying the filters of relevance and publication date, we organized the articles and prioritized those that directly addressed that matched the keywords and the use of bioinformatics tools. Additionally, we included studies focusing on in silico assays of biopesticides, such as molecular docking. Our review aimed to present a collection of recent literature on biopesticides against Drosophila suzukii, emphasizing bioinformatics methods. Through this work, we strive to contribute to the literature of new perspectives on the development and efficiency of biopesticides, along with to advance research that may improve pest control strategies.

RESULTS

In the results of the systematic review, we found 2734 articles related to the selected keywords. Six of these articles directly address Drosophila suzukii and the use of bioinformatics tools in the search for alternatives in pest control. In the selected studies, we observed that two articles tend to focus on phylogenetic approaches, searching for gene sequences, amino acids, and constructing phylogenetic trees. The other three articles used molecular modeling and docking of receptors such as GABA and TRP with plant-derived and synthetic compounds to study intermolecular interactions. However, we identified gaps in these studies that could lead to further research in the biorational development of biopesticides using bioinformatics tools.

Evaluation of Pregabalin bioadhesive multilayered microemulsion IOP-lowering eye drops

In spite of available treatment options, glaucoma continues to be a leading cause of irreversible blindness in the world. Current glaucoma medications have multiple limitations including: lack of sustained action; requirement for multiple dosing per day, ocular irritation and limited options for drugs with different mechanisms of action. Previously, we demonstrated that pregabalin, a drug with high affinity and selectivity for CACNA2D1, lowered IOP in a dose-dependent manner. The current study was designed to evaluate pregabalin microemulsion eye drops and to estimate its efficacy in humans using in silico methods. Molecular docking studies of pregabalin against CACNA2D1 of mouse, rabbit, and human were performed. Pregabalin microemulsion eye drops were characterized using multiple in vivo studies and its stability was evaluated over one year at different storage conditions. Molecular docking analyses and QSPR of pregabalin confirmed its suitability as a new IOP-lowering medication that functions using a new mechanism of action by binding to CACNA2D1 in all species evaluated. Because of its prolonged corneal residence time and corneal penetration enhancement, a single topical application of pregabalin ME can provide an extended IOP reduction of more than day in different animal models. Repeated daily dosing for 2 months confirms the lack of any tachyphylactic effect, which is a common drawback among marketed IOP-lowering medications. In addition, pregabalin microemulsion demonstrated good physical stability for one year, and chemical stability for 3-6 months if stored below 25 °C. Collectively, these outcomes greatly support the use of pregabalin eye drops as once daily IOP-lowering therapy for glaucoma management.

Computational and experimental approaches manifest the leishmanicidal potential of α-mangostin resourced from Garcinia cowa

Owing to the persistent number of parasitic deaths, Visceral leishmaniasis continues to haunt several economically weaker sections of India. The disease causes over 30,000 deaths and threatens millions annually on a global scale. The standard pentavalent antimonials, on the other hand, are associated with health adversities and disease relapse. The current study is focused on the search for the most potential natural bioactive phytocompound from the bark extract of the Northeastern Indian plant, Garcinia cowa, that shows potent anti-leishmanial properties. The High Resonance Liquid Chromatography followed by Mass Spectrometry (HR-LCMS) study followed by an in silico molecular docking using computational tools revealed that α-mangostin might potentially possess antiparasitic activity. To validate the anti-leishmanial efficacy of the compound, a cell viability assay was performed, which demonstrated the parasite-specific inhibitory activity of α-mangostin; with IC50 values ranging from 4.95 - 7.37 µM against the different forms of Leishmania donovani parasite. The flow cytometric analysis of the phytocompound treated parasites indicated an oxidative and nitrosative stress-mediated apoptotic cell death in the parasites, by the suggestive surge in nuclear fragmentation and mitochondrial dysfunction. Simultaneously, a cytokine profiling study suggested approximate two-to-three-fold upregulated levels of pro-inflammatory cytokines post-compound treatment, which is predicted to actively contribute to parasite-killing. α-mangostin was also found to reduce the chances of parasite survival by inhibiting arginase enzyme activity, which in favorable conditions facilitates its sustenance. This study thereby substantiates that α-mangostin significantly possesses anti-leishmanial potentiality that can be developed into a cure for this infectious disease.

Bergenin mitigates neuroinflammatory damage induced by high glucose: insights from Zebrafish, murine microbial cell line, and rat models

Background

The escalating global burden of diabetes and its associated cognitive impairment underscores the urgency for effective interventions. Bergenin shows promise in regulating glucose metabolism, mitigating inflammation, and improving cognitive function. Zebrafish models offer a unique platform for assessing drug efficacy and exploring pharmacological mechanisms, complemented by subsequent investigations in cell and rat models.

Methods

The experimental subjects included zebrafish larvae (CZ98:Tg (mpeg1:EGFP) ihb20Tg/+ ), adult zebrafish (immersed in 2% glucose), BV2 cell line (50 mM glucose + 10 μm Aβ1-42), and a streptozotocin (STZ) bilateral intracerebroventricular injection rat model. Bergenin's effects on the toxicity, behavior, and cognitive function of zebrafish larvae and adults were evaluated. The Morris water maze assessed cognitive function in rats. Neuronal histopathological changes were evaluated using HE and Nissl staining. qPCR and Western blot detected the expression of glycolysis enzymes, inflammatory factors, and Bergenin's regulation of PPAR/NF-κB pathway in these three models.

Results

1) In zebrafish larvae, Bergenin interventions significantly reduced glucose levels and increased survival rates while decreasing teratogenicity rates. Microglial cell fluorescence in the brain notably decreased, and altered swimming behavior tended to normalize. 2) In adult zebrafish, Bergenin administration reduced BMI and blood glucose levels, altered swimming behavior to slower speeds and more regular trajectories, enhanced recognition ability, decreased brain glucose and lactate levels, weakened glycolytic enzyme activities, improved pathological changes in the telencephalon and gills, reduced expression of pro-inflammatory cytokines, decreased ins expression and increased expression of irs1, irs2a, and irs2b, suggesting a reduction in insulin resistance. It also altered the expression of pparg and rela. 3) In BV2 cell line, Bergenin significantly reduced the protein expression of glycolytic enzymes (GLUT1, HK2, PKFKB3, and PKM2), lowered IL-1β, IL-6, and TNF-α mRNA expression, elevated PPAR-γ protein expression, and decreased P-NF-κB-p65 protein expression. 4) In the rat model, Bergenin improves learning and memory abilities in STZ-induced rats, mitigates neuronal damage in the hippocampal region, and reduces the expression of inflammatory factors IL-1β, IL-6, and TNF-α. Bergenin decreases brain glucose and lactate levels, as well as glycolytic enzyme activity. Furthermore, Bergenin increases PPARγ expression and decreases p-NF-κB p65/NF-κB p65 expression in the hippocampus.

Conclusion

Bergenin intervenes through the PPAR-γ/NF-κB pathway, redirecting glucose metabolism, alleviating inflammation, and preventing high glucose-induced neuronal damage.

Deepening insights into cholinergic agents for intraocular pressure reduction: systems genetics, molecular modeling, and in vivo perspectives

Parasympathetic activation in the anterior eye segment regulates various physiological functions. This process, mediated by muscarinic acetylcholine receptors, also impacts intraocular pressure (IOP) through the trabecular meshwork. While FDA-approved M3 muscarinic receptor (M3R) agonists exist for IOP reduction, their systemic cholinergic adverse effects pose limitations in clinical use. Therefore, advancing our understanding of the cholinergic system in the anterior segment of the eye is crucial for developing additional IOP-reducing agents with improved safety profiles. Systems genetics analyses were utilized to explore correlations between IOP and the five major muscarinic receptor subtypes. Molecular docking and dynamics simulations were applied to human M3R homology model using a comprehensive set of human M3R ligands and 1,667 FDA-approved or investigational drugs. Lead compounds from the modeling studies were then tested for their IOP-lowering abilities in mice. Systems genetics analyses unveiled positive correlations in mRNA expressions among the five major muscarinic receptor subtypes, with a negative correlation observed only in M3R with IOP. Through modeling studies, rivastigmine and edrophonium emerged as the most optimally suited cholinergic drugs for reducing IOP via a potentially distinct mechanism from pilocarpine or physostigmine. Subsequent animal studies confirmed comparable IOP reductions among rivastigmine, edrophonium, and pilocarpine, with longer durations of action for rivastigmine and edrophonium. Mild cholinergic adverse effects were observed with pilocarpine and rivastigmine but absent with edrophonium. These findings advance ocular therapeutics, suggesting a more nuanced role of the parasympathetic system in the anterior eye segment for reducing IOP than previously thought.

Proteins and Peptides Studied In Silico and In Vivo for the Treatment of Diabetes Mellitus: A Systematic Review

Bioinformatics has expedited the screening of new efficient therapeutic agents for diseases such as diabetes mellitus (DM). The objective of this systematic review (SR) was to understand naturally occurring proteins and peptides studied in silico and subsequently reevaluated in vivo for treating DM, guided by the question: which peptides or proteins have been studied in silico for the treatment of diabetes mellitus? The RS protocol was registered in the International Prospective Register of Systematic Reviews database. Articles meeting the eligibility criteria were selected from the PubMed, ScienceDirect, Scopus, Web of Science, Virtual Health Library (VHL), and EMBASE databases. Five studies that investigated peptides or proteins analyzed in silico and in vivo were selected. Risk of bias assessment was conducted using the adapted Strengthening the Reporting of Empirical Simulation Studies (STRESS) tool. A diverse range of assessed proteins and/or peptides that had a natural origin were investigated in silico and corresponding in vivo reevaluation demonstrated reductions in glycemia and/or insulin, morphological enhancements in pancreatic β cells, and alterations in the gene expression of markers associated with DM. The in silico studies outlined offer crucial insights into therapeutic strategies for DM, along with promising leads for screening novel therapeutic agents in future trials.

In silico studies of the open form of human tissue transglutaminase

Human tissue transglutaminase (tTG) is an intriguing multifunctional enzyme involved in various diseases, including celiac disease and neurological disorders. Although a number of tTG inhibitors have been developed, the molecular determinants governing ligand binding remain incomplete due to the lack of high-resolution structural data in the vicinity of its active site. In this study, we obtained the complete high-resolution model of tTG by in silico methods based on available PDB structures. We discovered significant differences in the active site architecture between our and known tTG models, revealing an additional loop which affects the ligand binding affinity. We assembled a library of new potential tTG inhibitors based on the obtained complete model of the enzyme. Our library substantially expands the spectrum of possible drug candidates targeting tTG and encompasses twelve molecular scaffolds, eleven of which are novel and exhibit higher binding affinity then already known ones, according to our in silico studies. The results of this study open new directions for structure-based drug design of tTG inhibitors, offering the complete protein model and suggesting a wide range of new compounds for further experimental validation.

Structural Studies of the Taurine Transporter: A Potential Biological Target from the GABA Transporter Subfamily in Cancer Therapy

The taurine transporter (TauT, SLC6A6) is a member of the solute carrier 6 (SLC6) family, which plays multiple physiological roles. The SLC6 family is divided into four subfamilies: GABA (γ-aminobutyric acid), monoamine, glycine and neutral amino acid transporters. Proteins from the GABA group, including the taurine transporter, are primarily considered therapeutic targets for treating central nervous system disorders. However, recent studies have suggested that inhibitors of SLC6A6 could also serve as anticancer agents. Overexpression of TauT has been associated with the progression of colon and gastric cancer. The pool of known ligands of this transporter is limited and the exact spatial structure of taurine transporter remains unsolved. Understanding its structure could aid in the development of novel inhibitors. Therefore, we utilized homology modelling techniques to create models of TauT. Docking studies and molecular dynamics simulations were conducted to describe protein-ligand interactions. We compared the obtained information for TauT with literature data on other members of the GABA transporter group. Our in silico analysis allowed us to characterize the transporter structure and point out amino acids crucial for ligand binding: Glu406, Gly62 and Tyr138. The significance of selected residues was confirmed through structural studies of mutants. These results will aid in the development of novel taurine transporter inhibitors, which can be explored as anticancer agents.

Molecular characterization and phylogenetic analyses of MetAP2 gene and protein of Nosema bombycis isolated from Guangdong, China

Background

Pebrine, caused by microsporidium Nosema bombycis, is a devastating disease that causes serious economic damages to the sericulture industry. Studies on development of therapeutic and diagnostic options for managing pebrine in silkworms are very limited. Methionine aminopeptidase type 2 (MetAP2) of microsporidia is an essential gene for their survival and has been exploited as the cellular target of drugs such as fumagillin and its analogues in several microsporidia spp., including Nosema of honeybees.

Methods

In the present study, using molecular and bioinformatics tools, we performed in-depth characterization and phylogenetic analyses of MetAP2 of Nosema bombycis isolated from Guangdong province of China.

Results

The full length of MetAP2 gene sequence of Nosema bombycis (Guangdong isolate) was found to be 1278 base pairs (bp), including an open reading frame of 1,077 bp, encoding a total of 358 amino acids. The bioinformatics analyses predicted the presence of typical alpha-helix structural elements, and absence of transmembrane domains and signal peptides. Additionally, other characteristics of a stable protein were also predicted. The homology-based 3D models of MetAP2 of Nosema bombycis (Guangdong isolate) with high accuracy and reliability were developed. The MetAP2 protein was expressed and purified. The observed molecular weight of MetAP2 protein was found to be ~43-45 kDa. The phylogenetic analyses showed that MetAP2 gene and amino acids sequences of Nosema bombycis (Guangdong isolate) shared a close evolutionary relationship with Nosema spp. of wild silkworms, but it was divergent from microsporidian spp. of other insects, Aspergillus spp., Saccharomyces cerevisiae, and higher animals including humans. These analyses indicated that the conservation and evolutionary relationships of MetAP2 are closely linked to the species relationships.

Conclusion

This study provides solid foundational information that could be helpful in optimization and development of diagnostic and treatment options for managing the threat of Nosema bombycis infection in sericulture industry of China.

Unraveling the Mechanisms of Cannabidiol's Pharmacological Actions: A Comprehensive Research Overview

Cannabis sativa has long been used for neurological and psychological healing. Recently, cannabidiol (CBD) extracted from cannabis sativa has gained prominence in the medical field due to its non-psychotropic therapeutic effects on the central and peripheral nervous systems. CBD, also acting as a potent antioxidant, displays diverse clinical properties such as anticancer, antiinflammatory, antidepressant, antioxidant, antiemetic, anxiolytic, antiepileptic, and antipsychotic effects. In this review, we summarized the structural activity relationship of CBD with different receptors by both experimental and computational techniques and investigated the mechanism of interaction between related receptors and CBD. The discovery of structural activity relationship between CBD and target receptors would provide a direction to optimize the scaffold of CBD and its derivatives, which would give potential medical applications on CBD-based therapies in various illnesses.

Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?

T cells are essential immune cells responsible for identifying and eliminating pathogens. Through interactions between their T-cell antigen receptors (TCRs) and antigens presented by major histocompatibility complex molecules (MHCs) or MHC-like molecules, T cells discriminate foreign and self peptides. Determining the fundamental principles that govern these interactions has important implications in numerous medical contexts. However, reconstructing a map between T cells and their antagonist antigens remains an open challenge for the field of immunology, and success of in silico reconstructions of this relationship has remained incremental. In this Perspective, we discuss the role that new state-of-the-art deep-learning models for predicting protein structure may play in resolving some of the unanswered questions the field faces linking TCR and peptide-MHC properties to T-cell specificity. We provide a comprehensive overview of structural databases and the evolution of predictive models, and highlight the breakthrough AlphaFold provided the field.

Diversity analysis of sea anemone peptide toxins in different tissues of Heteractis crispa based on transcriptomics

Peptide toxins found in sea anemones venom have diverse properties that make them important research subjects in the fields of pharmacology, neuroscience and biotechnology. This study used high-throughput sequencing technology to systematically analyze the venom components of the tentacles, column, and mesenterial filaments of sea anemone Heteractis crispa, revealing the diversity and complexity of sea anemone toxins in different tissues. A total of 1049 transcripts were identified and categorized into 60 families, of which 91.0% were proteins and 9.0% were peptides. Of those 1049 transcripts, 416, 291, and 307 putative proteins and peptide precursors were identified from tentacles, column, and mesenterial filaments respectively, while 428 were identified when the datasets were combined. Of these putative toxin sequences, 42 were detected in all three tissues, including 33 proteins and 9 peptides, with the majority of peptides being ShKT domain, β-defensin, and Kunitz-type. In addition, this study applied bioinformatics approaches to predict the family classification, 3D structures, and functional annotation of these representative peptides, as well as the evolutionary relationships between peptides, laying the foundation for the next step of peptide pharmacological activity research.

Exploring the Antarctic aminopeptidase P from Pseudomonas sp. strain AMS3 through structural analysis and molecular dynamics simulation

Aminopeptidase P (APPro) is a crucial metalloaminopeptidase involved in amino acid cleavage from peptide N-termini, playing essential roles as versatile biocatalysts with applications ranging from pharmaceuticals to industrial processes. Despite acknowledging its potential for catalysis in lower temperatures, detailed molecular basis and biotechnological implications in cold environments are yet to be explored. Therefore, this research aims to investigate the molecular mechanisms underlying the cold-adapted characteristics of APPro from Pseudomonas sp. strain AMS3 (AMS3-APPro) through a detailed analysis of its structure and dynamics. In this study, structure analysis and molecular dynamics (MD) simulation of a predicted model of AMS3-APPro has been performed at different temperatures to assess structural flexibility and thermostability across a temperature range of 0-60 °C over 100 ns. The MD simulation results revealed that the structure were able to remain stable at low temperatures. Increased temperatures present a potential threat to the overall stability of AMS3-APPro by disrupting the intricate hydrogen bond networks crucial for maintaining structural integrity, thereby increasing the likelihood of protein unfolding. While the metal binding site at the catalytic core exhibits resilience at higher temperatures, highlighting its local structural integrity, the overall enzyme structure undergoes fluctuations and potential denaturation. This extensive structural instability surpasses the localized stability observed at the metal binding site. Consequently, these assessments offer in-depth understanding of the cold-adapted characteristics of AMS3-APPro, highlighting its capability to uphold its native conformation and stability in low-temperature environments. In summary, this research provides valuable insights into the cold-adapted features of AMS3-APPro, suggesting its efficient operation in low thermal conditions, particularly relevant for potential biotechnological applications in cold environments.Communicated by Ramaswamy H. Sarma.

Reduction strategies of polycyclic aromatic hydrocarbons in farmland soils: Microbial degradation, plant transport inhibition, and their mechanistic analysis

This study focuses on the abatement of polycyclic aromatic hydrocarbons (PAHs), a global pollutant, in farmland soils. Seven controlled PAHs in China were used as the target ligands, and four key target receptors degradable PAHs and two key target receptors transport PAHs were used as the target receptors. Firstly, the degradation abilities of the four key target receptors on PAHs were quantified, and the dominant target receptors that could efficiently degrade PAHs were screened out. Then, the co-degradation abilities of PAHs under the coexistence of the dominant target receptors (microbial diversity) were assessed, and 30 external condition-adding schemes to promote the microbial (co-)degradation of PAHs were designed. In addition, the microbial dominant target receptor mutants and the plant key target receptor mutants were obtained, the degradation and transportation of PAHs were improved by 8.06%∼22.27% and 39.86%∼45.43%. Finally, the mechanism analysis of PAHs biodegradation and transportation found that the Van der Waals interactions dominated the enhancement of PAHs' degradation in soil, and the solvation capacity dominated the decrease of PAHs' transportation in plant. This study aims to provide theoretical support for the prevention and control of PAHs residue pollution in farmland soil, as well as the protection of human dietary health.

Screening of traditional medicinal plant extracts and compounds identifies a potent anti-leishmanial diarylheptanoid from Siphonochilus aethiopicus

Available anti-leishmanial drugs are associated with toxic side effects, necessitating the search for safe and effective alternatives. This study is focused on identifying traditional medicinal plant natural products for anti-leishmanial potential and possible mechanism of action. Compounds S and T. cordifolia residual fraction (TC-5) presented the best anti-leishmanial activity (IC50: 0.446 and 1.028 mg/ml) against promastigotes at 48 h and less cytotoxicity to THP-1 macrophages. These test agents elicited increased expression of pro-inflammatory cytokines; TNFα and IL-12. In infected untreated macrophages, NO release was suppressed but was significantly (p < 0.05) increased in infected cells treated with compound S. Importantly, Compound S was found to interact with LdTopoIIdimer in silico, resulting in a likely reduced ability of nucleic acid (dsDNA)-remodelling and, as a result, parasite proliferation in vitro. Thereby, Compound S possesses anti-leishmanial activity and this effect occurs via a Th1-mediated pro-inflammatory response. An increase in NO release and its inhibitory effect on LdTopoII may also contribute to the anti-leishmanial effect of compound S. These results show the potential of this compound as a potential starting point for the discovery of novel anti-leishmanial leads.Communicated by Ramaswamy H. Sarma.

Reducing Immunogenicity by Design: Approaches to Minimize Immunogenicity of Monoclonal Antibodies

Monoclonal antibodies (mAbs) have transformed therapeutic strategies for various diseases. Their high specificity to target antigens makes them ideal therapeutic agents for certain diseases. However, a challenge to their application in clinical practice is their potential risk to induce unwanted immune response, termed immunogenicity. This challenge drives the continued efforts to deimmunize these protein therapeutics while maintaining their pharmacokinetic properties and therapeutic efficacy. Because mAbs hold a central position in therapeutic strategies against an array of diseases, the importance of conducting comprehensive immunogenicity risk assessment during the drug development process cannot be overstated. Such assessment necessitates the employment of in silico, in vitro, and in vivo strategies to evaluate the immunogenicity risk of mAbs. Understanding the intricacies of the mechanisms that drive mAb immunogenicity is crucial to improving their therapeutic efficacy and safety and developing the most effective strategies to determine and mitigate their immunogenic risk. This review highlights recent advances in immunogenicity prediction strategies, with a focus on protein engineering strategies used throughout development to reduce immunogenicity.

Network analysis-guided drug repurposing strategies targeting LPAR receptor in the interplay of COVID, Alzheimer's, and diabetes

The COVID-19 pandemic caused by the SARS-CoV-2 virus has greatly affected global health. Emerging evidence suggests a complex interplay between Alzheimer's disease (AD), diabetes (DM), and COVID-19. Given COVID-19's involvement in the increased risk of other diseases, there is an urgent need to identify novel targets and drugs to combat these interconnected health challenges. Lysophosphatidic acid receptors (LPARs), belonging to the G protein-coupled receptor family, have been implicated in various pathological conditions, including inflammation. In this regard, the study aimed to investigate the involvement of LPARs (specifically LPAR1, 3, 6) in the tri-directional relationship between AD, DM, and COVID-19 through network analysis, as well as explore the therapeutic potential of selected anti-AD, anti-DM drugs as LPAR, SPIKE antagonists. We used the Coremine Medical database to identify genes related to DM, AD, and COVID-19. Furthermore, STRING analysis was used to identify the interacting partners of LPAR1, LPAR3, and LPAR6. Additionally, a literature search revealed 78 drugs on the market or in clinical studies that were used for treating either AD or DM. We carried out docking analysis of these drugs against the LPAR1, LPAR3, and LPAR6. Furthermore, we modeled the LPAR1, LPAR3, and LPAR6 in a complex with the COVID-19 spike protein and performed a docking study of selected drugs with the LPAR-Spike complex. The analysis revealed 177 common genes implicated in AD, DM, and COVID-19. Protein-protein docking analysis demonstrated that LPAR (1,3 & 6) efficiently binds with the viral SPIKE protein, suggesting them as targets for viral infection. Furthermore, docking analysis of the anti-AD and anti-DM drugs against LPARs, SPIKE protein, and the LPARs-SPIKE complex revealed promising candidates, including lupron, neflamapimod, and nilotinib, stating the importance of drug repurposing in the drug discovery process. These drugs exhibited the ability to bind and inhibit the LPAR receptor activity and the SPIKE protein and interfere with LPAR-SPIKE protein interaction. Through a combined network and targeted-based therapeutic intervention approach, this study has identified several drugs that could be repurposed for treating COVID-19 due to their expected interference with LPAR(1, 3, and 6) and spike protein complexes. In addition, it can also be hypothesized that the co-administration of these identified drugs during COVID-19 infection may not only help mitigate the impact of the virus but also potentially contribute to the prevention or management of post-COVID complications related to AD and DM.

Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges

Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.

Unlocking Opportunities for Mycobacterium leprae and Mycobacterium ulcerans

In the recent decade, scientific communities have toiled to tackle the emerging burden of drug-resistant tuberculosis (DR-TB) and rapidly growing opportunistic nontuberculous mycobacteria (NTM). Among these, two neglected mycobacteria species of the Acinetobacter family, Mycobacterium leprae and Mycobacterium ulcerans, are the etiological agents of leprosy and Buruli ulcer infections, respectively, and fall under the broad umbrella of neglected tropical diseases (NTDs). Unfortunately, lackluster drug discovery efforts have been made against these pathogenic bacteria in the recent decade, resulting in the discovery of only a few countable hits and majorly repurposing anti-TB drug candidates such as telacebec (Q203), P218, and TB47 for current therapeutic interventions. Major ignorance in drug candidate identification might aggravate the dramatic consequences of rapidly spreading mycobacterial NTDs in the coming days. Therefore, this Review focuses on an up-to-date account of drug discovery efforts targeting selected druggable targets from both bacilli, including the accompanying challenges that have been identified and are responsible for the slow drug discovery. Furthermore, a succinct discussion of the all-new possibilities that could be alternative solutions to mitigate the neglected mycobacterial NTD burden and subsequently accelerate the drug discovery effort is also included. We anticipate that the state-of-the-art strategies discussed here may attract major attention from the scientific community to navigate and expand the roadmap for the discovery of next-generation therapeutics against these NTDs.

Potential molecular mechanism of reuterin on the inhibition of Aspergillus flavus conidial germination: An in silico study

Reuterin is a natural antifungal agent derived from certain strains of Limosilactobacillus reuteri. Our previous study revealed that 6 mM reuterin inhibited completely the conidial germination of aflatoxigenic Aspergillus flavus. This study investigated the potential molecular mechanism of reuterin in inhibiting A. flavus conidial germination, which was pre-assumed that it correlated to the inhibition of some essential enzyme activity involved in conidial germination, specifically 1,3-β-glucan synthase, chitin synthase, and catalases (catalase, bifunctional catalase-peroxidase, and spore-specific catalase). The complex of 1,3-β-glucan synthase and chitin synthase with reuterin had a lower binding affinity than that with the substrate. Conversely, the complex of catalases with reuterin had a higher binding affinity than that with the substrate. It was suggested that 1,3-β-glucan synthase and chitin synthase tended to bind the substrate rather than bind reuterin. In contrast, catalases tended to bind reuterin rather than bind the substrate. Therefore, reuterin could be a potential inhibitor of catalases but may not be an inhibitor of 1,3-β-glucan synthase and chitin synthase. In this in silico study, we predicted that the potential molecular mechanism of reuterin in inhibiting A. flavus conidial germination was due to the inhibition of catalases activities by competitively binding to the enzymes active sites, thus resulting in the accumulation of reactive oxygen species in cells, leading to cells damage. PRACTICAL APPLICATION: This in silico study revealed that reuterin is a potential inhibitor of catalases in A. flavus, thereby interfering with the antioxidant system during conidial germination. This finding shows that reuterin can be used as an antifungal agent in food or agricultural products, inhibiting conidial germination completely.

Development and use of machine learning algorithms in vaccine target selection

Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.

HTLV-1 reverse transcriptase homology model provides structural basis for sensitivity to existing nucleoside/nucleotide reverse transcriptase inhibitors

The human T-lymphotropic virus type 1 (HTLV-1) infects millions of people globally and is endemic to various resource-limited regions. Infections persist for life and are associated with increased susceptibility to opportunistic infections and severe diseases including adult T cell leukemia/lymphoma and HTLV-1-associated myelopathy-tropical spastic paraparesis. No HTLV-1-specific anti-retrovirals have been developed and it is unclear whether existing anti-retrovirals developed for treatment of human immunodeficiency virus (HIV) have efficacy against HTLV-1. To understand the structural basis for therapeutic binding, homology modelling and machine learning were used to develop a structural model of the HTLV-1 reverse transcriptase. With this, molecular docking experiments using a panel of FDA-approved inhibitors of viral reverse transcriptases to assess their capacity for binding, and in turn, inhibition. Importantly, nucleoside/nucleotide reverse transcriptase inhibitor but not non-nucleoside reverse transcriptase inhibitors were predicted to bind the HTLV-1 reverse transcriptase, with similar affinity to HIV-1 reverse transcriptase. By strengthening the rationale for clinical testing of therapies such as tenofovir alafenamide, zidovudine, lamivudine, and azvudine for treatment of HTLV-1, this study has demonstrated the power of in silico structural biology approaches in drug design and therapeutic testing.

Diversity and Evolutionary Analysis of Venom Insulin Derived from Cone Snails

Cone snails possess a diverse array of novel peptide toxins, which selectively target ion channels and receptors in the nervous and cardiovascular systems. These numerous novel peptide toxins are a valuable resource for future marine drug development. In this review, we compared and analyzed the sequence diversity, three-dimensional structural variations, and evolutionary aspects of venom insulin derived from different cone snail species. The comparative analysis reveals that there are significant variations in the sequences and three-dimensional structures of venom insulins from cone snails with different feeding habits. Notably, the venom insulin of some piscivorous cone snails exhibits a greater similarity to humans and zebrafish insulins. It is important to emphasize that these venom insulins play a crucial role in the predatory strategies of these cone snails. Furthermore, a phylogenetic tree was constructed to trace the lineage of venom insulin sequences, shedding light on the evolutionary interconnections among cone snails with diverse diets.

Halotolerant Bacteria from Genus Nesterenkonia sandarakina VSA9 as a Potential Polyhydroxyalkanoate Producer

Nesterenkonia sandarakina VSA9 pigmented bacteria isolated from Sargassum is being reported to produce polyhydroxyalkanoates (PHA) deduced through detecting the presence of pha C gene using the molecular method. The PHA synthase gene was of type I which has been concluded from the phylogenetic tree and multiple sequence analysis. The amino acid analysis of pha C gene confirms the involvement of the lipase box having a sequence of G-Y-C-I-G-G with cysteine as the active center of the PHA synthase. Homology modeling predicted the 3D protein structure which is similar to the PHA synthase of Chromobacterium sp. USM2. The solvent extract of N. sandarakina VSA9 showed the presence of Carotenoid compound with maximum wavelength at 475 nm. The study's findings could have far-reaching implications, contributing to advancements in the biotechnology, industrial processes, and sustainable practices. The simultaneous production of carotenoids and PHAs by N. sandarakina VSA9 presents exciting opportunities for the development of innovative and environmentally friendly applications.

Ligand 1 H NMR Chemical Shifts as Accurate Reporters for Protein-Ligand Binding Interfaces in Solution

The availability of high-resolution 3D structural information is crucial for investigating guest-host systems across a wide range of fields. In the context of drug discovery, the information is routinely used to establish and validate structure-activity relationships, grow initial hits from screening campaigns, and to guide molecular docking. For the generation of protein-ligand complex structural information, X-ray crystallography is the experimental method of choice, however, with limited information on protein flexibility. An experimentally verified structural model of the binding interface in the native solution-state would support medicinal chemists in their molecular design decisions. Here we demonstrate that protein-bound ligand 1 H NMR chemical shifts are highly sensitive and accurate probes for the immediate chemical environment of protein-ligand interfaces. By comparing the experimental ligand 1 H chemical shift values with those computed from the X-ray structure using quantum mechanics methodology, we identify significant disagreements for parts of the ligand between the two experimental techniques. We show that quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) ensembles can be used to refine initial X-ray co-crystal structures resulting in a better agreement with experimental 1 H ligand chemical shift values. Overall, our findings highlight the usefulness of ligand 1 H NMR chemical shift information in combination with a QM/MM MD workflow for generating protein-ligand ensembles that accurately reproduce solution structural data.

Molecular Modeling Techniques and In-Silico Drug Discovery

Molecular modeling is the technique to determine the overall structure of an unknown molecule, be it a small one or a macromolecule. The technique encompasses the method of screening ligand libraries for the development of new candidate drug molecules. All these aspects have become an essential topic of research. This field is truly interdisciplinary and finds its applications in almost all fields of life science research. In this chapter, an overview of the protocol associated with molecular modeling techniques will be discussed.

Identification of novel umami peptides from yeast extract and the mechanism against T1R1/T1R3

Yeast extract was separated by using ultrafiltration, gel filtration chromatography, and preparative high-performance liquid chromatography for analyzing the umami mechanism. 13 kinds of umami peptides were screened out from 73 kinds of peptides which were identified in yeast extract using nanoscale ultra-performance liquid chromatography-tandem mass spectrometry and virtual screening. The umami peptides were found to have a threshold range of 0.07-0.61 mM. DWTDDVEAR exhibited a strong umami taste with a pronounced enhancement effect for monosodium glutamate. Molecular docking studies revealed that specific amino acid residues in the T1R1 subunit, including Arg316, Ser401, and Asp315, played a critical role in the umami perception with these peptides. Overall, the study highlights the potential of natural flavor enhancers and provides insights into the mechanism of umami taste perception.

Assessing the fast prediction of peptide conformers and the impact of non-natural modifications

We present an assessment of different approaches to predict peptide structures using modeling tools. Several small molecule, protein, and peptide-focused methodologies were used for the fast prediction of conformers for peptides shorter than 30 amino acids. We assessed the effect of including restraints based on annotated or predicted secondary structure motifs. A number of peptides in bound conformations and in solution were collected to compare the tools. In addition, we studied the impact of changing single amino acids to non-natural residues using molecular dynamics simulations. Deep learning methods such as AlphaFold2, or the combination of physics-based approaches with secondary structure information, produce the most accurate results for natural sequences. In the case of peptides with non-natural modifications, modeling the peptide containing natural amino acids first and then modifying and simulating the peptide using benchmarked force fields is a recommended pipeline. The results can guide the modeling of oligopeptides for drug discovery projects.

Naringenin improves muscle endurance via activation of the Sp1-ERRγ transcriptional axis

Skeletal muscle function declines in the aging process or disease; however, until now, skeletal muscle has remained one of the organs most undertreated with medication. In this study, naringenin (NAR) was found to build muscle endurance in wild-type mice of different ages by increasing oxidative myofiber numbers and aerobic metabolism, and it ameliorates muscle dysfunction in mdx mice. The transcription factor Sp1 was identified as a direct target of NAR and was shown to mediate the function of NAR on muscle. Moreover, the binding site of NAR on Sp1 was further validated as GLN-110. NAR enhances the binding of Sp1 to the CCCTGCCCTC sequence of the Esrrg promoter by promoting Sp1 phosphorylation, thus upregulating Esrrg expression. The identification of the Sp1-ERRγ transcriptional axis is of great significance in basic muscle research, and this function of NAR has potential implications for the improvement of muscle function and the prevention of muscle atrophy.

Genetic insights into obesity: in silico identification of pathogenic SNPs in MBOAT4 gene and their structural molecular dynamics consequences

Membrane Bound O-Acyltransferase Domain-Containing 4 (MBOAT4) protein catalyzes ghrelin acylation, leading to prominent ghrelin activity, hence characterizing its role as an anti-obesity target. We extracted 625 exonic SNPs from the ENSEMBL database and one phenotype-based missense mutation associated with obesity (A46T) from the HGMD (Human Gene Mutation Database). These were differentiated on deleterious missense SNPs of the MBOAT4 gene through MAF (minor allele frequency: <0.01) cut-off criteria in relation to some bioinformatics-based supervised machine learning tools. We found 8 rare-coding and harmful missense SNPs. The consensus classifier (PredictSNP) tool predicted that the SNP (G57S, C: rs561065025) was the most pathogenic. Several trained in silico algorithms have predicted decreased protein stability [ΔΔG (kcal/mol)] function in the presence of these rare-coding pathogenic mutations in the MBOAT4 gene. Then, a stereochemical quality check (i.e. validation and assessment) of the 3D model was performed, followed by a blind cavity docking approach, used to search for druggable cavities and molecular interactions with citrus flavonoids of the Rutaceae family, ranked with energetic estimations. Significant interactions with Phloretin 3',5'-Di-C-Glucoside were also observed at R304, W306, N307, A311, L314 and H338 with (iGEMDOCK: -95.82 kcal/mol and AutoDock: -7.80 kcal/mol). The RMSD values and other variables of MD simulation analyses on this protein further validated its significant interactions with the above flavonoids. The MBOAT4 gene and its molecular interactions could serve as an interventional future anti-obesity target. The current study's findings will benefit future prospects for large population-based studies and drug development, particularly for generating personalized medicine.Communicated by Ramaswamy H. Sarma.

High-throughput virtual screening approach of natural compounds as target inhibitors of plasmepsin-II

Plasmepsin II is a key enzyme in the life cycle of the Plasmodium falciparum parasite responsible for malaria, a disease that is causing deaths on a worldwide scale. Recently, plasmepsin II enzyme has gained much importance as an attractive drug target for the investigation of antimalarial drugs. In this sense, structure-based virtual screening have been utilized as tools in the process of discovering novel natural compounds based on quinoline as potential plasmepsin II inhibitors. Among the 58 quinoline derivatives isolated from different plants was screened by utilizing docking molecular, ADMET approaches, molecular dynamics simulation and MM-PBSA binding free energy. The first step in this work is building the 3 D structures of the plasmepsin II enzyme by using the SWISS-MODEL software. The optimized structures were subjected to virtual screening by Autodock Vina, an entity implicated in PyRx software. 21 were selected based on their binding affinity. The binding modes and interactions of the top-21 selected compounds were evaluated using AutoDock 4.2. Then, the pharmacokinetic proprieties and toxicity of these compounds were evaluated using ADMET analysis. Ten compounds were predicted to have ADMET characteristics with no side effects. Compounds M49 and M53 were found to be potential inhibitors. The stability of the selected two compounds was confirmed by MD simulation and MM/PBSA calculation during 200 ns. This study can be used to predict and to design new antimalarial drugs.Communicated by Ramaswamy H. Sarma.

Antiemetic effects of sclareol, possibly through 5-HT3 and D2 receptor interaction pathways: In-vivo and in-silico studies

BACKGROUND

Emesis is a complex physiological phenomenon that serves as a defense against numerous toxins, stressful situations, adverse medication responses, chemotherapy, and movement. Nevertheless, preventing emesis during chemotherapy or other situations is a significant issue for researchers. Hence, the majority view contends that successfully combining therapy is the best course of action. In-vivo analysis offers a more comprehensive grasp of how compounds behave within a complex biological environment, whereas in-silico evaluation refers to the use of computational models to forecast biological interactions.

OBJECTIVES

The objectives of the present study were to evaluate the effects of Sclareol (SCL) on copper sulphate-induced emetic chicks and to investigate the combined effects of these compounds using a conventional co-treatment approach and in-silico study.

METHODS

SCL (5, 10, and 15 mg/kg) administered orally with or without pre-treatment with anti-emetic drugs (Ondansetron (ODN): 24 mg/kg, Domperidone (DOM): 80 mg/kg, Hyoscine butylbromide (HYS): 100 mg/kg, and Promethazine hydrochloride (PRO): 100 mg/kg) to illustrate the effects and the potential involvement with 5HT3, D2, M3/AChM, H1, or NK1 receptors by SCL. Furthermore, an in-silico analysis was conducted to forecast the role of these receptors in the emetic process.

RESULTS

The results suggest that SCL exerted a dose-dependent anti-emetic effect on the chicks. Pretreatment with SCL-10 significantly minimized the number of retches and lengthened the emesis tendency of the experimental animals. SCL-10 significantly increased the anti-emetic effects of ODN and DOM. However, compared to the ODN-treated group, (SCL-10 + ODN) group considerably (p < 0.0001) extended the latency duration (109.40 ± 1.03 s) and significantly (p < 0.01) decreased the number of retches (20.00 ± 0.70), indicating an anti-emetic effect on the test animals. In in-silico analysis, SCL exhibited promising binding affinities with suggesting receptors.

CONCLUSION

SCL-10 exerted an inhibitory-like effect on emetic chicks, probably through the interaction of the 5HT3 and D2 receptors. Further studies are highly appreciated to validate this study and determine the precise mechanism(s) behind the anti-emetic effects of SCL. We expect that SCL-10 may be utilized as an antiemetic treatment in a single dosage form or that it may function as a synergist with other traditional medicines.

Molecular Docking-Based Identification of Potential Natural Neuroprotective Molecules for Parkinson's Disease

BACKGROUND

Parkinson's disease (PD) is a common progressive neurodegenerative and the prevailing treatments are ineffective in the early stages of the disease. Therefore, other strategies must be devised to halt the steady decrease of dopaminergic neurons in the brain. In Parkinson's disease, a dysregulated ACE/Ang II/AT1R axis in the brain causes free radical damage, apoptosis, and neuronal destruction. Current PD treatments only alleviate symptoms and do not reverse the degradation mechanism of dopaminergic neurons. As a result, it is critical to discover alternate, dependable medicines for the treatment of Parkinson's disease.

METHOD

In the present study, homology modelling of MAS receptor, in silico docking and molecular dynamic studies (MDS) were employed to determine the efficacy of flavonoids as MASR activators.

RESULT

The flavonoids Pterosupin and Amentoflavone exhibited best binding and therefore, the stability of these complexes were evaluated with MDS studies. The Pterosupin-MASR complex demonstrated better stability, stronger interactions and minimal fluctuation than the Amentoflavone-MASR complex.

CONCLUSION

The data from the present study indicated that the flavonoid Pterosupin possesses better binding, favourable pharmacokinetic properties and stability. However, subsequent in vitro and in vivo assessments are necessary to validate its efficacy.

Advancements in small molecule drug design: A structural perspective

In this review, we outline recent advancements in small molecule drug design from a structural perspective. We compare protein structure prediction methods and explore the role of the ligand binding pocket in structure-based drug design. We examine various structural features used to optimize drug candidates, including functional groups, stereochemistry, and molecular weight. Computational tools such as molecular docking and virtual screening are discussed for predicting and optimizing drug candidate structures. We present examples of drug candidates designed based on their molecular structure and discuss future directions in the field. By effectively integrating structural information with other valuable data sources, we can improve the drug discovery process, leading to the identification of novel therapeutics with improved efficacy, specificity, and safety profiles.

SARS-CoV-2 proteins structural studies using synchrotron radiation

In the process of the development of structural biology, both the size and the complexity of the determined macromolecular structures have grown significantly. As a result, the range of application areas for the results of structural studies of biological macromolecules has expanded. Significant progress in the development of structural biology methods has been largely achieved through the use of synchrotron radiation. Modern sources of synchrotron radiation allow to conduct high-performance structural studies with high temporal and spatial resolution. Thus, modern techniques make it possible to obtain not only static structures, but also to study dynamic processes, which play a key role in understanding biological mechanisms. One of the key directions in the development of structural research is the drug design based on the structures of biomolecules. Synchrotron radiation offers insights into the three-dimensional time-resolved structure of individual viral proteins and their complexes at atomic resolution. The rapid and accurate determination of protein structures is crucial for understanding viral pathogenicity and designing targeted therapeutics. Through the application of experimental techniques, including X-ray crystallography and small-angle X-ray scattering (SAXS), it is possible to elucidate the structural details of SARS-CoV-2 virion containing 4 structural, 16 nonstructural proteins (nsp), and several accessory proteins. The most studied potential targets for vaccines and drugs are the structural spike (S) protein, which is responsible for entering the host cell, as well as nonstructural proteins essential for replication and transcription, such as main protease (Mpro), papain-like protease (PLpro), and RNA-dependent RNA polymerase (RdRp). This article provides a brief overview of structural analysis techniques, with focus on synchrotron radiation-based methods applied to the analysis of SARS-CoV-2 proteins.

Evaluation of in vitro effect, molecular docking, and molecular dynamics simulations of some dihydropyridine-class calcium channel blockers on human serum paraoxonase 1 (hPON1) enzyme activity

Paraoxonase 1 (PON1) was purified 148.80-fold in 37.92% yield by hydrophobic interaction chromatography technique. The purity of PON1 was checked by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) with a single band of 43 kDa. The in vitro effects of nine different calcium channel blockers on PON1 activity were evaluated. All drugs strongly decreased PON1 activity, and IC50 levels were between 13.987 ± 0.59 and 238.104 ± 2.14 μM, Ki values between 8.58 ± 0.36 and 111 ± 1.27 μM. The drugs with the strongest inhibitory effect were nisoldipine with 13.987 ± 0.59 μM and nicardipine with 20.158 ± 0.43 μM. The mechanism of action for the inhibition of the enzyme by nisoldipine and nicardipine was investigated through molecular docking. The stability of enzyme-ligand complexes obtained from the docking was explored through molecular dynamics simulation. The binding affinity of the ligands toward the enzyme was also investigated through MMPBSA (molecular mechanics Poisson-Boltzmann surface area method). The computational analysis demonstrated these compounds could inhibit the enzyme. Nisoldipine had the strongest binding, and its complex was the most stable one. Furthermore, nicardipine was found to have the highest affinity toward the enzyme.

Orphan G Protein-Coupled Receptor GPR37 as an Emerging Therapeutic Target

G protein-coupled receptors (GPCRs) are successful druggable targets, making up around 35% of all FDA-approved medications. However, a large number of receptors remain orphaned, with no known endogenous ligand, representing a challenging but untapped area to discover new therapeutic targets. Among orphan GPCRs (oGPCRs) of interest, G protein-coupled receptor 37 (GPR37) is highly expressed in the central nervous system (CNS), particularly in the spinal cord and oligodendrocytes. While its cellular signaling mechanisms and endogenous receptor ligands remain elusive, GPR37 has been implicated in several important neurological conditions, including Parkinson's disease (PD), inflammation, pain, autism, and brain tumors. GPR37 structure, signaling, emerging physiology, and pharmacology are reviewed while integrating a discussion on potential therapeutic indications and opportunities.

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

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.

CEMIP (HYBID, KIAA1199): structure, function and expression in health and disease

CEMIP (cell migration-inducing protein), also known as KIAA1199 or HYBID, is a protein involved in the depolymerisation of hyaluronic acid (HA), a major glycosaminoglycan component of the extracellular matrix. CEMIP was originally described in patients affected by nonsyndromic hearing loss and has subsequently been shown to play a key role in tumour initiation and progression, as well as arthritis, atherosclerosis and idiopathic pulmonary fibrosis. Despite the vast literature associating CEMIP with these diseases, its biology remains elusive. The present review article summarises all the major scientific evidence regarding its structure, function, role and expression, and attempts to cast light on a protein that modulates EMT, fibrosis and tissue inflammation, an unmet key aspect in several inflammatory disease conditions.

Molecular Modeling Unveils the Effective Interaction of B-RAF Inhibitors with Rare B-RAF Insertion Variants

The Food and Drug Administration (FDA) has approved MAPK inhibitors as a treatment for melanoma patients carrying a mutation in codon V600 of the BRAF gene exclusively. However, BRAF mutations outside the V600 codon may occur in a small percentage of melanomas. Although these rare variants may cause B-RAF activation, their predictive response to B-RAF inhibitor treatments is still poorly understood. We exploited an integrated approach for mutation detection, tumor evolution tracking, and assessment of response to treatment in a metastatic melanoma patient carrying the rare p.T599dup B-RAF mutation. He was addressed to Dabrafenib/Trametinib targeted therapy, showing an initial dramatic response. In parallel, in-silico ligand-based homology modeling was set up and performed on this and an additional B-RAF rare variant (p.A598_T599insV) to unveil and justify the success of the B-RAF inhibitory activity of Dabrafenib, showing that it could adeptly bind both these variants in a similar manner to how it binds and inhibits the V600E mutant. These findings open up the possibility of broadening the spectrum of BRAF inhibitor-sensitive mutations beyond mutations at codon V600, suggesting that B-RAF V600 WT melanomas should undergo more specific investigations before ruling out the possibility of targeted therapy.

Quercetin Antagonizes the Sedative Effects of Linalool, Possibly through the GABAergic Interaction Pathway

Sedatives promote calmness or sleepiness during surgery or severely stressful events. In addition, depression is a mental health issue that negatively affects emotional well-being. A group of drugs called anti-depressants is used to treat major depressive illnesses. The aim of the present work was to evaluate the effects of quercetin (QUR) and linalool (LIN) on thiopental sodium (TS)-induced sleeping mice and to investigate the combined effects of these compounds using a conventional co-treatment strategy and in silico studies. For this, the TS-induced sleeping mice were monitored to compare the occurrence, latency, and duration of the sleep-in response to QUR (10, 25, 50 mg/kg), LIN (10, 25, 50 mg/kg), and diazepam (DZP, 3 mg/kg, i.p.). Moreover, an in silico investigation was undertaken to assess this study's putative modulatory sedation mechanism. For this, we observed the ability of test and standard medications to interact with various gamma-aminobutyric acid A receptor (GABA_A) subunits. Results revealed that QUR and LIN cause dose-dependent antidepressant-like and sedative-like effects in animals, respectively. In addition, QUR-50 mg/kg and LIN-50 mg/kg and/or DZP-3 mg/kg combined were associated with an increased latency period and reduced sleeping times in animals. Results of the in silico studies demonstrated that QUR has better binding interaction with GABA_A α3, β1, and γ2 subunits when compared with DZP, whereas LIN showed moderate affinity with the GABA_A receptor. Taken together, the sleep duration of LIN and DZP is opposed by QUR in TS-induced sleeping mice, suggesting that QUR may be responsible for providing sedation-antagonizing effects through the GABAergic interaction pathway.

In silico characterization of the psilocybin biosynthesis pathway

Nearly all mushrooms of the Psilocybe genus contain the natural product psilocybin, which is a psychoactive alkaloid derived from l-tryptophan. Considering their use in ancient times, as well as their psychedelic properties, these mushrooms have re-emerged with psychotherapeutic potential for treating depression, which has triggered increased pharmaceutical interest. However, the psilocybin biosynthesis pathway was only recently defined and, as such, little exists in the way of structural data. Accordingly, the aim of this study was to structurally characterize this pathway by generating homology models for the four Psilocybe cubensis enzymes involved in psilocybin biosynthesis (PsiD, a decarboxylase; PsiH, a monooxygenase; PsiK, a phosphotransferase; PsiM, a methyltransferase). Following initial model generation and alignment with the identified structural templates, repeated refinement of the models was carried out using secondary structure prediction, geometry evaluation, energy minimization, and molecular dynamics simulations in water. The final models were then evaluated using molecular docking interactions with their substrates, i.e., psilocybin precursors (l-tryptophan, tryptamine, 4-hydroxytryptamine, and norbaeocystin/baeocystin), all of which generated feasible binding modes for the expected biotransformation. Further plausibility of the psilocybin → aeruginascin, 4-hydroxytryptamine → norpsilocin, and tryptamine → N,N-dimethyltryptamine conversions, all mediated by the generated model for PsiM, suggests valid routes of formation for these key secondary metabolites. The structural characterization of these enzymes and their binding modes which emerged from this study can lead to a better understanding of psilocybin synthesis, thereby paving the way for the development of novel substrates and selective inhibitors, as well as improved biotechnological manipulation and production of psilocybin in vitro.

Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels

Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and currently available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development. This review summarizes important knowledge about ion channel classification, structure, mechanisms, and pathology with the main focus on recent developments in the field of computer-aided, structure-based drug design on ion channels. We highlight studies that link structural data with modeling and chemoinformatic approaches for the identification and characterization of new molecules targeting ion channels. These approaches hold great potential to advance research on ion channel drugs in the future.

Evaluating Biofilm Inhibitory Potential in Fish Pathogen, Aeromonas hydrophila by Agricultural Waste Extracts and Assessment of Aerolysin Inhibitors Using In Silico Approach

Aeromonas hydrophila, an opportunistic bacteria, causes several devastating diseases in humans and animals, particularly aquatic species. Antibiotics have been constrained by the rise of antibiotic resistance caused by drug overuse. Therefore, new strategies are required to prevent appropriate antibiotic inability from antibiotic-resistant strains. Aerolysin is essential for A. hydrophila pathogenesis and has been proposed as a potential target for inventing drugs with anti-virulence properties. It is a unique method of disease prevention in fish to block the quorum-sensing mechanism of A. hydrophila. In SEM analysis, the crude solvent extracts of both groundnut shells and black gram pods exhibited a reduction of aerolysin formation and biofilm matrix formation by blocking the QS in A. hydrophila. Morphological changes were identified in the extracts treated bacterial cells. Furthermore, in previous studies, 34 ligands were identified with potential antibacterial metabolites from agricultural wastes, groundnut shells, and black gram pods using a literature survey. Twelve potent metabolites showed interactions between aerolysin and metabolites during molecular docking analysis, in that H-Pyran-4-one-2,3 dihydro-3,5 dihydroxy-6-methyl (-5.3 kcal/mol) and 2-Hexyldecanoic acid (-5.2 kcal/mol) showed promising results with potential hydrogen bond interactions with aerolysin. These metabolites showed a better binding affinity with aerolysin for 100 ns in molecular simulation dynamics. These findings point to a novel strategy for developing drugs using metabolites from agricultural wastes that may be feasible pharmacological solutions for treating A. hydrophila infections for the betterment of aquaculture.

The nucleotide excision repair proteins through the lens of molecular dynamics simulations

Mutations that affect the proteins responsible for the nucleotide excision repair (NER) pathway can lead to diseases such as xeroderma pigmentosum, trichothiodystrophy, Cockayne syndrome, and Cerebro-oculo-facio-skeletal syndrome. Hence, understanding their molecular behavior is needed to elucidate these diseases' phenotypes and how the NER pathway is organized and coordinated. Molecular dynamics techniques enable the study of different protein conformations, adaptable to any research question, shedding light on the dynamics of biomolecules. However, as important as they are, molecular dynamics studies focused on DNA repair pathways are still becoming more widespread. Currently, there are no review articles compiling the advancements made in molecular dynamics approaches applied to NER and discussing: (i) how this technique is currently employed in the field of DNA repair, focusing on NER proteins; (ii) which technical setups are being employed, their strengths and limitations; (iii) which insights or information are they providing to understand the NER pathway or NER-associated proteins; (iv) which open questions would be suited for this technique to answer; and (v) where can we go from here. These questions become even more crucial considering the numerous 3D structures published regarding the NER pathway's proteins in recent years. In this work, we tackle each one of these questions, revising and critically discussing the results published in the context of the NER pathway.

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.