Molecular Mechanism for the Allosteric Inhibition of the Human Serotonin Transporter by Antidepressant Escitalopram

The binding pattern of ferric iron and iron-binding protein in Botrytis cinerea

Iron-binding protein (Ibp) has protective effect on pathogen exposed to H2O2 in defense response of plants. Ibp in Botrytis cinerea (BcIbp) is related to its virulence. Bcibp mutation lead to virulence deficiencies in B. cinerea. BcIbp is involved in the Fe3+ homeostasis regulation. Recognition the binding site and binding pattern of ferric iron and iron-binding protein in B. cinerea are vital to understand its function. In this study, molecular dynamics (MD) simulations, gaussian accelerated molecular dynamics (GaMD) simulations, dynamic cross correlation analysis and quantum chemical energy calculation were used to explore binding pattern of ferric iron. MD results showed that the C-terminal region had little effect on the stability of residues in the Fe3+-binding pocket. Energy calculations suggested the most likely coordination pattern for ferric iron in iron-binding protein. These results will help to understand the binding of ferric iron to iron-binding protein and provide new ideas for regulating the virulence of B. cinerea.

Potential anti-liver cancer targets and mechanisms of kaempferitrin based on network pharmacology, molecular docking and experimental verification

AIM

Kaempferitrin is an active component in Chenopodium ambrosioides, showing medicinal functions against liver cancer. This study aimed to identify the potential targets and pathways of kaempferitrin against liver cancer using network pharmacology and molecular docking, and verify the essential hub targets and pathway in mice model of SMMC-7721 cells xenografted tumors and SMMC-7721 cells.

METHODS

Kaempferitrin therapeutical targets were obtained by searching SwissTargetPrediction, PharmMapper, STITCH, DrugBank, and TTD databases. Liver cancer specific genes were obtained by searching GeneCards, DrugBank, TTD, OMIM, and DisGeNET databases. PPI network of "kaempferitrin-targets-liver cancer" was constructed to screen the hub targets. GO, KEGG pathway and MCODE clustering analyses were performed to identify possible enrichment of genes with specific biological subjects. Molecular docking and molecular dynamics simulation were employed to determine the docking pose, potential and stability of kaempferitrin with hub targets. The potential anti-liver cancer mechanisms of kaempferitrin, as predicted by network pharmacology analyses, were verified by in vitro and in vivo experiments.

RESULTS

228 kaempferitrin targets and 2186 liver cancer specific targets were identified, of which 50 targets were overlapped. 8 hub targets were identified through network topology analysis, and only SIRT1 and TP53 had a potent binding activity with kaempferitrin as indicated by molecular docking and molecular dynamics simulation. MCODE clustering analysis revealed the most significant functional module of PPI network including SIRT1 and TP53 was mainly related to cell apoptosis. GO and KEGG enrichment analyses suggested that kaempferitrin exerted therapeutic effects on liver cancer possibly by promoting apoptosis via p21/Bcl-2/Caspase 3 signaling pathway, which were confirmed by in vivo and in vitro experiments, such as HE staining of tumor tissues, CCK-8, qRT-PCR and Western blot.

CONCLUSION

This study provided not only insight into how kaempferitrin could act against liver cancer by identifying hub targets and their associated signaling pathways, but also experimental evidence for the clinical use of kaempferitrin in liver cancer treatment.

DeLA-DrugSelf: Empowering multi-objective de novo design through SELFIES molecular representation

In this paper, we introduce DeLA-DrugSelf, an upgraded version of DeLA-Drug [J. Chem. Inf. Model. 62 (2022) 1411-1424], which incorporates essential advancements for automated multi-objective de novo design. Unlike its predecessor, which relies on SMILES notation for molecular representation, DeLA-DrugSelf employs a novel and robust molecular representation string named SELFIES (SELF-referencing Embedded String). The generation process in DeLA-DrugSelf not only involves substitutions to the initial string representing the starting query molecule but also incorporates insertions and deletions. This enhancement makes DeLA-DrugSelf significantly more adept at executing data-driven scaffold decoration and lead optimization strategies. Remarkably, DeLA-DrugSelf explicitly addresses the SELFIES-related collapse issue, considering only collapse-free compounds during generation. These compounds undergo a rigorous quality metrics evaluation, highlighting substantial advancements in terms of drug-likeness, uniqueness, and novelty compared to the molecules generated by the previous version of the algorithm. To evaluate the potential of DeLA-DrugSelf as a mutational operator within a genetic algorithm framework for multi-objective optimization, we employed a fitness function based on Pareto dominance. Our objectives focused on target-oriented properties aimed at optimizing known cannabinoid receptor 2 (CB2R) ligands. The results obtained indicate that DeLA-DrugSelf, available as a user-friendly web platform (https://www.ba.ic.cnr.it/softwareic/delaself/), can effectively contribute to the data-driven optimization of starting bioactive molecules based on user-defined parameters.

DTNPD: A comprehensive database of drugs and targets for neurological and psychiatric disorders

In response to the shortcomings in data quality and coverage for neurological and psychiatric disorders (NPDs) in existing comprehensive databases, this paper introduces the DTNPD database, specifically designed for NPDs. DTNPD contains detailed information on 30 NPDs types, 1847 drugs, 514 drug targets, 64 drug combinations, and 61 potential target combinations, forming a network with 2389 drug-target associations. The database is user-friendly, offering open access and downloadable data, which is crucial for network pharmacology studies. The key strength of DTNPD lies in its robust networks of drug and target combinations, as well as drug-target networks, facilitating research and development in the field of NPDs. The development of the DTNPD database marks a significant milestone in understanding and treating NPDs. For accessing the DTNPD database, the primary URL is http://dtnpd.cnsdrug.com, complemented by a mirror site available at http://dtnpd.lyhbio.com.

Bioactive compounds from Ocimum tenuiflorum and Poria cocos: A novel natural Compound for insomnia treatment based on A computational approach

Insomnia, a widespread public health issue, is associated with substantial distress and daytime functionality impairments and can predispose to depression and cardiovascular disease. Cognitive Behavioral Anti-insomnia therapies including benzodiazepines often face limitations due to patient adherence or potential adverse effects. This study focused on identifying novel bioactive compounds from medicinal plants, aiming to discover and develop new therapeutic agents with low risk-to-benefit ratios using computational drug discovery methods. Through a systematic framework involving compound library preparation, evaluation of drug-likeness and pharmacokinetics, toxicity prediction, molecular docking, and molecular dynamic simulations, two natural compounds such as 2-(4-hydroxy-3-methoxyphenyl)-8-methoxy-6-prop-2-enyl-3,4-dihydro-2H-chromen-3-ol from Ocimum tenuiflorum and 7-(2-hydroxypropan-2-yl)-1,4a-dimethyl-9-oxo-3,4,10,10a-tetrahydro-2H-phenanthrene-1-carboxylic acid from Poria cocos exhibited high binding affinity with orexin receptor type 1 (OX1R) and type 2 (OX2R), surpassing commercial drugs used in insomnia treatment. Additionally, they showed interactions with critical amino acid residues within the receptors that play crucial roles in competitive inhibitor activity, like commercial drugs such as Suvorexant, Lemborexant, and Daridorexant. Further, molecular dynamics simulations of the protein-ligand complexes under conditions that mimic the in vivo environment revealed both compounds' sustained and robust interactions with the OX1R and OX2R, reinforcing their potential as effective therapeutic candidates. Furthermore, upon evaluating both compounds' drug-likeness, pharmacokinetics, and toxicity profiles, it was discerned that they displayed considerable drug-like properties and favorable pharmacokinetics, along with diminished toxicity. The research provides a solid foundation for further exploring and validating these compounds as potential anti-insomnia therapeutics.

The safety and efficacy of escitalopram and sertraline in post-stroke depression: a randomized controlled trial

OBJECTIVES

This study aims to evaluate the safety and efficacy of escitalopram and sertraline in post-stroke depression (PSD) patients, to provide more reliable therapeutics for cardiovascular and psychiatric clinical practice.

METHODS

We recruited 60 patients (aged 40-89 years old) with an ICD-10 diagnosis of PSD, who were then randomly assigned to two groups and treated with flexible doses of escitalopram (10 to 20 mg/day, n = 30) or sertraline (50 to 200 mg/day, n = 30) for consecutive 8 weeks, respectively. The 24-item Hamilton Depression Rating Scale (HAMD-24), the 14-item Hamilton Anxiety Rating Scale (HAMA-14), the Treatment Emergent Symptom Scale (TESS), the Montreal Cognitive Assessment Scale (MOCA), and the Activity of Daily Living scale (ADL) were used to assess patients before, during, and after treatment for depression, anxiety, adverse effects, cognitive function, and daily living activities. Repeated measures ANOVA, the Mann-Whitney U test, the chi-square test (χ2), or Fisher's exact test was employed to assess baseline demographics, response rate, adverse effects rate, and changes in other clinical variables.

RESULTS

Significant reduction in HAMD-24 and HAMA-14 scores was evaluated at baseline, as well as 1, 3, 4, 6, and 8 weeks of drug intervention (p < 0.01). There was a significant group difference in post-treatment HAMD-24 scores (p < 0.05), but no difference was observed in HAMA-14 scores (p > 0.05). Further analysis showed a significant variance in the HAMD-24 scores between the two groups at the end of the first week (p < 0.01). The incidence of adverse effects in both patient groups was mild, but there was a statistically significant difference between the two groups (p < 0.05). The improvement in cognitive function and the recovery of daily living abilities were comparable between both groups (p > 0.05).

CONCLUSION

Escitalopram and sertraline showed comparable efficacy for anxiety symptoms, cognitive function, and daily living abilities in PSD patients. In addition, escitalopram was more appropriate for alleviating depressive symptoms. To validate the conclusion, trials with a larger sample size are in demand in the future. The registration number is ChiCTR1800017373.

FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response

Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia-reperfusion injury and other pathological cell death. The regulation of ferroptosis mainly focuses on three pathways: system Xc-/GPX4 axis, lipid peroxidation and iron metabolism. The genes involved in these processes were divided into driver, suppressor and marker. Importantly, small molecules or drugs that mediate the expression of these genes are often good treatments in the clinic. Herein, a newly developed database, named 'FERREG', is documented to (i) providing the data of ferroptosis-related regulation of diseases occurrence, progression and drug response; (ii) explicitly describing the molecular mechanisms underlying each regulation; and (iii) fully referencing the collected data by cross-linking them to available databases. Collectively, FERREG contains 51 targets, 718 regulators, 445 ferroptosis-related drugs and 158 ferroptosis-related disease responses. FERREG can be accessed at https://idrblab.org/ferreg/.

Allosteric modulation of serotonin and dopamine transporters: New insights from computations and experiments

Human monoamine transporters (MATs) are critical to regulating monoaminergic neurotransmission by translocating their substrates from the synaptic space back into the presynaptic neurons. As such, their primary substrate binding site S1 has been targeted by a wide range of compounds for treating neuropsychiatric and neurodegenerative disorders including depression, ADHD, neuropathic pain, and anxiety disorders. We present here a comparative study of the structural dynamics and ligand-binding properties of two MATs, dopamine transporter (DAT) and serotonin transporter (SERT), with focus on the allosteric modulation of their transport function by drugs or substrates that consistently bind a secondary site S2, proposed to serve as an allosteric site. Our systematic analysis of the conformational space and dynamics of a dataset of 50 structures resolved for DAT and SERT in the presence of one or more ligands/drugs reveals the specific residues playing a consistent role in coordinating the small molecules bound to subsites S2-I and S2-II within S2, such as R476 and Y481 in dDAT and E494, P561, and F556 in hSERT. Further analysis reveals how DAT and SERT differ in their two principal modes of structural changes, PC1 and PC2. Notably, PC1 underlies the transition between outward- and inward-facing states of the transporters as well as their gating; whereas PC2 supports the rearrangements of TM helices near the S2 site. Finally, the examination of cross-correlations between structural elements lining the respective sites S1 and S2 point to the crucial role of coupled motions between TM6a and TM10. In particular, we note the involvement of hSERT residues F335 and G338, and E493-E494-T497 belonging to these two respective helices, in establishing the allosteric communication between S1 and S2. These results help understand the molecular basis of the action of drugs that bind to the S2 site of DAT or SERT. They also provide a basis for designing allosteric modulators that may provide better control of specific interactions and cellular pathways, rather than indiscriminately inhibiting the transporter by targeting its orthosteric site.

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

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

Is fragment-based graph a better graph-based molecular representation for drug design? A comparison study of graph-based models

Graph Neural Networks (GNNs) have gained significant traction in various sectors of AI-driven drug design. Over recent years, the integration of fragmentation concepts into GNNs has emerged as a potent strategy to augment the efficacy of molecular generative models. Nonetheless, challenges such as symmetry breaking and potential misrepresentation of intricate cycles and undefined functional groups raise questions about the superiority of fragment-based graph representation over traditional methods. In our research, we undertook a rigorous evaluation, contrasting the predictive prowess of eight models-developed using deep learning algorithms-across 12 benchmark datasets that span a range of properties. These models encompass established methods like GCN, AttentiveFP, and D-MPNN, as well as innovative fragment-based representation techniques. Our results indicate that fragment-based methodologies, notably PharmHGT, significantly improve model performance and interpretability, particularly in scenarios characterized by limited data availability. However, in situations with extensive training, fragment-based molecular graph representations may not necessarily eclipse traditional methods. In summation, we posit that the integration of fragmentation, as an avant-garde technique in drug design, harbors considerable promise for the future of AI-enhanced drug design.

Characterizing the Molecular Mechanism of the Lethal C423D Mutation in FgMyoI: A Molecular Perspective

The lethal mutation C423D in Fusarium graminearum myosin I (FgMyoI) occurs close to the binding pocket of the allosteric inhibitor phenamacril and causes severe inhibition on mycelial growth of F. graminearum strain PH-1. Here, based on extensive Gaussian accelerated molecular dynamics simulations and wet experiments, we elucidate the underlying molecular mechanism of the abnormal functioning of the FgMyoIC423D mutant at the atomistic level. Our results suggest that the damaging mutation C423D exhibits a synergistic allosteric inhibition mechanism similar to but more robust than that of phenamacril, including effects on the active site and actin binding. Unlike phenamacril-induced closure of Switch2, the mutation results in unfolding of the N-terminal relay helix with a partially opened Switch2 and blocks the structural rearrangement of the relay/SH1 helices, impairing the proper initiation of the recovery stroke. Due to the significant influence of C423D mutation on the function of FgMyoI, designing covalent inhibitors targeting this site holds tremendous potential.

VARIDT 3.0: the phenotypic and regulatory variability of drug transporter

The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.

TheMarker: a comprehensive database of therapeutic biomarkers

Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.

Unraveling the allosteric inhibition mechanism of PARP-1 CAT and the D766/770A mutation effects via Gaussian accelerated molecular dynamics and Markov state model

PARP-1 (Poly (ADP-ribose) polymerase 1) is a nuclear enzyme and plays a key role in many cellular functions, such as DNA repair, modulation of chromatin structure, and recombination. Developing the PARP-1 inhibitors has emerged as an effective therapeutic strategy for a growing list of cancers. The catalytic structural domain (CAT) of PARP-1 upon binding the inhibitor allosterically regulates the conformational changes of helix domain (HD), affecting its identification with the damaged DNA. The typical type I (EB47) and III (veliparib) inhibitors were able to lengthening or shortening the retention time of this enzyme on DNA damage and thus regulating the cytotoxicity. Nonetheless, the basis underlying allosteric inhibition is unclear, which limits the development of novel PARP-1 inhibitors. Here, to investigate the distinct allosteric changes of EB47 and veliparib against PARP-1 CAT, each complex was simulated via classical and Gaussian accelerated molecular dynamics (cMD and GaMD). To study the reverse allosteric basis and mutation effects, the complexes PARP-1 with UKTT15 and PARP-1 D766/770A mutant with EB47 were also simulated. Importantly, the markov state models were built to identify the transition pathways of crucial substates of allosteric communication and the induction basis of PARP-1 reverse allostery. The conformational change differences of PARP-1 CAT regulated by allosteric inhibitors were concerned with to their interaction at the active site. Energy calculations suggested the energy advantage of EB47 in inhibiting the wild-type PARP-1, compared with D766/770A PARP-1. Secondary structure results showed the change of two key loops (αB-αD and αE-αF) in different systems. This work reported the basis of PARP-1 allostery from both thermodynamic and kinetic views, providing the guidance for the discovery and design of more innovative PARP-1 allosteric inhibitors.

Allosteric inhibition of myosin by phenamacril: a synergistic mechanism revealed by computational and experimental approaches

BACKGROUND

Myosin plays a crucial role in cellular processes, while its dysfunction can lead to organismal malfunction. Phenamacril (PHA), a highly species-specific and non-competitive inhibitor of myosin I (FgMyoI) from Fusarium graminearum, has been identified as an effective fungicide for controlling plant diseases caused by partial Fusarium pathogens, such as wheat scab and rice bakanae. However, the molecular basis of its action is still unclear.

RESULTS

This study used multiple computational approaches first to elucidate the allosteric inhibition mechanism of FgMyoI by PHA at the atomistic level. The results indicated the increase of adenosine triphosphate (ATP) binding affinity upon PHA binding, which might impede the release of hydrolysis products. Furthermore, simulations revealed a broadened outer cleft and a significantly more flexible interface for actin binding, accompanied by a decrease in signaling transduction from the catalytic center to the actin-binding interface. These various effects might work together to disrupt the actomyosin cycle and hinder the ability of motor to generate force. Our experimental results further confirmed that PHA reduces the enzymatic activity of myosin and its binding with actin.

CONCLUSION

Therefore, our findings demonstrated that PHA might suppress the function of myosin through a synergistic mechanism, providing new insights into myosin allostery and offering new avenues for drug/fungicide discovery targeting myosin. © 2023 Society of Chemical Industry.

Targeting the omicron variant of SARS-CoV-2 with phytochemicals from Saudi medicinal plants: molecular docking combined with molecular dynamics investigations

The new health crises caused by SARS-CoV-2 have resulted in millions of deaths worldwide. First discovered in November 2021, the omicron variant is more transmissible and is able to evade the immune system better than other previously identified SARS-CoV-2 variants, leading to a spike in cases. Great efforts have been made to discover inhibitors against SARS-CoV-2. Main protease (Mpro) inhibitors are considered promising anti-SARS-CoV-2 agents. The U.S. FDA has issued an Emergency Use Authorization for ritonavir-boosted nirmatrelvir. Nirmatrelvir is the first orally bioavailable inhibitor of SARS-CoV-2 Mpro. There is an urgent need to monitor the mutations and solve the problem of resistance, especially omicron Mpro, which contains one mutation - P132H. In the present study, 132,57 phytochemicals from 80 medicinal plants grown in Saudi Arabia were docked into the active site of Mpro omicron variant. Free binding energies were also calculated. This led to the discovery of five phytochemicals that showed better docking scores than the bound ligand nirmatrelvir. In addition, these molecules exhibited favorable free binding energies. The stability of compounds 1-5 with the protein was studied using molecular dynamics (MD) simulations. These compounds showed acceptable ADMET properties. The results were compared with the wild type. These candidates could be envisioned as new hits against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

HPTLC based quantification of β-sitosterol from the leaves of Nyctanthes arbor-tristis and in-silico prediction of potential drug targeted towards cancer therapy

Nyctanthes arbor-tristis (N. arbor-tristis) is a plant of enormous medicinal importance and each part of the plant retained extensive medicinal properties, such as antimicrobial, antioxidant, anticancer and anti-inflammatory activities, etc. Phytosterols are secondary metabolites of plants that are well-known for their ability to reduce cholesterol, boost immunity and inhibit the formation of cancer cells. In this study, β-sitosterol, which boosts antioxidant enzymes and reduces oxidative stress, was qualitatively and quantitatively identified in the methanolic extract of the plant's leaves using the HPTLC. Our findings show that N. arbor-tristis has a significant concentration of β-sitosterol with the 0.64 Rf value. Further, docking and simulation (in-silico) studies have shown that β-sitosterol has good binding affinity with human DNA Topoisomerase I (h-DNA Topo I) and has the potential to inhibit its activity. It can be reconstructed as h-DNA Topo I determent.Communicated by Ramaswamy H. Sarma.

The Important Role of Transporter Structures in Drug Disposition, Efficacy, and Toxicity

The ATP-binding cassette (ABC) and solute carrier (SLC) transporters are critical determinants of drug disposition, clinical efficacy, and toxicity as they specifically mediate the influx and efflux of various substrates and drugs. ABC transporters can modulate the pharmacokinetics of many drugs via mediating the translocation of drugs across biologic membranes. SLC transporters are important drug targets involved in the uptake of a broad range of compounds across the membrane. However, high-resolution experimental structures have been reported for a very limited number of transporters, which limits the study of their physiologic functions. In this review, we collected structural information on ABC and SLC transporters and described the application of computational methods in structure prediction. Taking P-glycoprotein (ABCB1) and serotonin transporter (SLC6A4) as examples, we assessed the pivotal role of structure in transport mechanisms, details of ligand-receptor interactions, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms. The data collected contributes toward safer and more effective pharmacological treatments. SIGNIFICANCE STATEMENT: The experimental structure of ATP-binding cassette and solute carrier transporters was collected, and the application of computational methods in structure prediction was described. P-glycoprotein and serotonin transporter were used as examples to reveal the pivotal role of structure in transport mechanisms, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms.

Inhibitory selectivity to the AKR1B10 and aldose reductase (AR): insight from molecular dynamics simulations and free energy calculations

AKR1B10 is over-expressed in many cancer types and is related to chemotherapy resistance, which makes AKR1B10 a potential anti-cancer target. The high similarity of the protein structure between AKR1B10 and AR makes it difficult to develop highly selective inhibitors against AKR1B10. Understanding the interaction between AKR1B10 and inhibitors is very important for designing selective inhibitors of AKR1B10. In this study, Fidarestat, Zopolrestat, MK184 and MK204 bound to AKR1B10 and AR were used to investigate the selectivity mechanism. The results of MM/PBSA calculations show that van der Waals and electrostatic interaction provide the main contributions of the binding free energy. The hydrogen bonding between residues Y49 and H111 and inhibitors plays a pivotal role in contributing to the high inhibitory activity of AKR1B10 inhibitors. The π-π stacking interaction between residue W112 and inhibitor also plays a key role in the stability of inhibitors and AKR1B10, but W112 should keep its natural conformation to stabilize the inhibitor-AKR1B10 complex. Highly selective AKR1B10 inhibitors should have a bulky moiety like a phenyl group, which can change its binding with ABP in binding with AR and cannot change its binding with AKR1B10. The free energy decomposition shows that residues W21, V48, Y49, K78, W80, H111, R298 and V302 are beneficial to the stability of the inhibitor-AKR1B10. Our work will provide an important in silico basis for researchers to develop highly selective inhibitors of AKR1B10.

Cupressus sempervirens L. flavonoids as potent inhibitors to xanthine oxidase: in vitro, molecular docking, ADMET and PASS studies

Excessive intake of purine-rich foods such as seafood and red meat leads to excess xanthine oxidase activity and provokes gout attacks. The aim of this paper is to evaluate in vitro and in silico, the inhibition effect of Cupressus sempervirens plant extracts (flavonoids (Cae) and alkaloids (CaK)) and its six derivative compounds on bovine xanthine oxidase (BXO). The in silico study consists of molecular docking with GOLD v4.0 based on the best PLPchem score (PLP) and prediction of biological activity with the PASS server tool. The inhibitors used were lignan (cp1), Amentoflavone (cp2), Cupressuflavone (cp3), Isocryptomerin (cp4), Hinokiflavone (cp5), and Neolignan (cp6). The in vitro results showed that CaK gives an IC50 of 3.52 ± 0.04 μg/ml. Similarly, Cae saved an IC50 of 8.46 ± 1.98 μg/ml compared with the control (2.82 ± 0.10 μg/ml). The in silico results show that cp1 was the best inhibitor model (PLP of 88.09) with approved pharmacokinetics. These findings suggest that cp1 and cp2 may offer good alternatives for the treatment of hyperuricemia; cp3 was moderate, while the others (cp4 to cp6) were considered weak inhibitors according to their PLP.Communicated by Ramaswamy H. Sarma.

Probing the molecular mechanism of interaction between polystyrene nanoplastics and catalase by multispectroscopic techniques

Nanoplastics are emerging pollutants that pose a potential threat to the environment and organisms and are widely distributed in environmental samples and food chains. The accumulation of polystyrene nanoplastics (PS-NPs) in an organism can cause oxidative stress. Currently, toxicity studies of PS-NPs mainly focus on the individual and cellular levels, whereas few studies have been conducted on the molecular mechanisms of the interaction between PS-NPs and catalase (CAT). Based on this, CAT was chosen as the target receptor for molecular toxicity research to reveal the interaction mechanism at the molecular level between PS-NPs and CAT by using various spectroscopic means and enzyme activity detection methods. The results indicated that PS-NPs destroyed the secondary structure of CAT, causing its protein skeleton to loosen and unfold, increasing the content of α-helices, decreasing the content of β-sheets, and exposing the position of the heme group. After exposure to PS-NPs, the internal fluorophore of CAT underwent fluorescence sensitization, resulting in a micelle-like structure, which enhanced the hydrophobicity of aromatic amino acids but did not change their polarity. In addition, the aggregation state of CAT was altered upon binding to PS-NPs, and the volume was further increased. Finally, these structural changes led to a gradual decrease in CAT activity. This study presents a comprehensive assessment of the toxicity of PS-NPs at the molecular level, which can provide more experimental support for the study of the biotoxicological efficacy of PS-NPs.

Rm-LR: A long-range-based deep learning model for predicting multiple types of RNA modifications

Recent research has highlighted the pivotal role of RNA post-transcriptional modifications in the regulation of RNA expression and function. Accurate identification of RNA modification sites is important for understanding RNA function. In this study, we propose a novel RNA modification prediction method, namely Rm-LR, which leverages a long-range-based deep learning approach to accurately predict multiple types of RNA modifications using RNA sequences only. Rm-LR incorporates two large-scale RNA language pre-trained models to capture discriminative sequential information and learn local important features, which are subsequently integrated through a bilinear attention network. Rm-LR supports a total of ten RNA modification types (m6A, m1A, m5C, m5U, m6Am, Ψ, Am, Cm, Gm, and Um) and significantly outperforms the state-of-the-art methods in terms of predictive capability on benchmark datasets. Experimental results show the effectiveness and superiority of Rm-LR in prediction of various RNA modifications, demonstrating the strong adaptability and robustness of our proposed model. We demonstrate that RNA language pretrained models enable to learn dense biological sequential representations from large-scale long-range RNA corpus, and meanwhile enhance the interpretability of the models. This work contributes to the development of accurate and reliable computational models for RNA modification prediction, providing insights into the complex landscape of RNA modifications.

Exploring the natural products chemical space through a molecular search to discover potential inhibitors that target the hypoxia-inducible factor (HIF) prolyl hydroxylase domain (PHD)

Introduction: Hypoxia-inducible factor (HIF) prolyl hydroxylase domain (PHD) enzymes are major therapeutic targets of anemia and ischemic/hypoxia diseases. To overcome safety issues, liver failure, and problems associated with on-/off-targets, natural products due to their novel and unique structures offer promising alternatives as drug targets. Methods: In the current study, the Marine Natural Products, North African, South African, East African, and North-East African chemical space was explored for HIF-PHD inhibitors discovery through molecular search, and the final hits were validated using molecular simulation and free energy calculation approaches. Results: Our results revealed that CMNPD13808 with a docking score of -8.690 kcal/mol, CID15081178 with a docking score of -8.027 kcal/mol, CID71496944 with a docking score of -8.48 kcal/mol and CID11821407 with a docking score of -7.78 kcal/mol possess stronger activity than the control N-[(4-hydroxy-8-iodoisoquinolin-3-yl)carbonyl]glycine, 4HG (-6.87 kcal/mol). Interaction analysis revealed that the target compounds interact with Gln239, Tyr310, Tyr329, Arg383 and Trp389 residues, and chelate the active site iron in a bidentate manner in PHD2. Molecular simulation revealed that these target hits robustly block the PHD2 active site by demonstrating stable dynamics. Furthermore, the half-life of the Arg383 hydrogen bond with the target ligands, which is an important factor for PHD2 inhibition, remained almost constant in all the complexes during the simulation. Finally, the total binding free energy of each complex was calculated as CMNPD13808-PHD2 -72.91 kcal/mol, CID15081178-PHD2 -65.55 kcal/mol, CID71496944-PHD2 -68.47 kcal/mol, and CID11821407-PHD2 -62.06 kcal/mol, respectively. Conclusion: The results show the compounds possess good activity in contrast to the control drug (4HG) and need further in vitro and in vivo validation for possible usage as potential drugs against HIF-PHD2-associated diseases.

Structure-Based Discovery of a Novel Allosteric Inhibitor against Human Dopamine Transporter

Human dopamine transporter (hDAT) regulates the reuptake of extracellular dopamine (DA) and is an essential therapeutic target for central nervous system (CNS) diseases. The allosteric modulation of hDAT has been identified for decades. However, the molecular mechanism underlying the transportation is still elusive, which hinders the rational design of allosteric modulators against hDAT. Here, a systematic structure-based method was performed to explore allosteric sites on hDAT in inward-open (IO) conformation and to screen compounds with allosteric affinity. First, the model of the hDAT structure was constructed based on the recently reported Cryo-EM structure of the human serotonin transporter (hSERT) and Gaussian-accelerated molecular dynamics (GaMD) simulation was further utilized for the identification of intermediate energetic stable states of the transporter. Then, with the potential druggable allosteric site on hDAT in IO conformation, virtual screening of seven enamine chemical libraries (∼440,000 compounds) was processed, resulting in 10 compounds being purchased for in vitro assay and with Z1078601926 discovered to allosterically inhibit hDAT (IC₅₀ = 0.527 [0.284; 0.988] μM) when nomifensine was introduced as an orthosteric ligand. Finally, the synergistic effect underlying the allosteric inhibition of hDAT by Z1078601926 and nomifensine was explored using additional GaMD simulation and postbinding free energy analysis. The hit compound discovered in this work not only provides a good starting point for lead optimization but also demonstrates the usability of the method for the structure-based discovery of novel allosteric modulators of other therapeutic targets.

Deciphering the intrinsic dynamics of unphosphorylated IRAK4 kinase bound to type I and type II inhibitors

Interleukin-1 receptor-associated kinase 4 (IRAK4) is a vital protein involved in Toll-like and interleukin-1 receptor signal transduction. Several studies have reported regarding the crystal structure, dynamic properties, and interactions with inhibitors of the phosphorylated form of IRAK4. However, no dynamic properties of inhibitor-bound unphosphorylated IRAK4 have been previously studied. Herein, we report the intrinsic dynamics of unphosphorylated IRAK4 (uIRAK4) bound to type I and type II inhibitors. The corresponding apo and inhibitor-bound forms of uIRAK4 were subjected to three independent simulations of 500 ns (total 1.5 μs) each, and their trajectories were analyzed. The results indicated that all three systems were relatively stable, except for the type II inhibitor-bound form of uIRAK4, which exhibited less compact folding and higher solvent surface area. The intra-hydrogen bonds corroborated the structural deformation of the type-II inhibitor-bound complex, which could be attributed to the long molecular structure of the type-II inhibitor. Moreover, the type II inhibitor bound to uIRAK4 showed higher binding free energy with uIRAK4 than the type I inhibitor. The free energy landscape analysis showed a reorientation of Phe330 side chain from the DFG motif at different metastable states for all the systems. The intra-residual distance between residues Lys213, Glu233, Tyr262, and Phe330 suggests a functional interplay when the inhibitors are bound to uIRAK4, thereby hinting at their crucial role in the inhibition mechanism. Ultimately, the intrinsic dynamics study observed between type I/II inhibitor-bound forms of uIRAK4 may assist in better understanding the enzyme and designing therapeutic compounds.

Structure based virtual screening, molecular dynamic simulation to identify the oxadiazole derivatives as inhibitors of Enterococcus D-Ala-D-Ser ligase for combating vancomycin resistance

Vancomycin resistance in enterococci mainly arises due to alteration in terminal peptidoglycan dipeptide. A comprehensive structural analysis for substrate specificity of dipeptide modifying d-Alanine: d-Serine ligase (Ddls) is essential to screen its inhibitors for combating vancomycin resistance. In this study modeled 3D structure of EgDdls from E. gallinarum was used for structure based virtual screening (SBVS) of oxadiazole derivatives. Initially, fifteen oxadiazole derivatives were identified as inhibitors at the active site of EgDdls from PubChem database. Further, four EgDdls inhibitors were evaluated using pharmacokinetic profile and molecular docking. The results of molecular docking showed that oxadiazole inhibitors could bind preferentially at ATP binding pocket with the lowest binding energy. Further, molecular dynamics simulation results showed stable behavior of EgDdls in complex with screened inhibitors. The residues Phe172, Lys174, Glu217, Phe292, and Asn302 of EgDdls were mainly involved in interactions with screened inhibitors. Furthermore, MM-PBSA calculation showed electrostatic and van der Waals interactions mainly contribute to overall binding energy. The PCA analysis showed motion of central domain and omega loop of EgDdls. This is involved in the formation of native dipeptide and stabilized after binding of 2-(1-(Ethylsulfonyl) piperidin-4-yl)-5-(furan-2-yl)-1,3,4-oxadiazole, which could be reason for the inhibition of EgDdls. Hence, in this study we have screened inhibitors of EgDdls which could be useful to alleviate the vancomycin resistance problem in enterococci, involved in hospital-acquired infections, especially urinary tract infections (UTI).

An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties

The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol^-1 to 2.45 kcal mol^-1, showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.

Deciphering the mechanism of resistance by novel double mutations in pncA in Mycobacterium tuberculosis using protein structural graphs (PSG) and structural bioinformatic approaches

The evolution of MDR and XDR-TB is a growing concern and public health safety threat around the world. Gene mutations are the prime cause of drug resistance in tuberculosis, however the reports of double mutations further aggravated the situation. Despite the large-scale genomic sequencing and identification of novel mutations, structure investigation of the protein is still required to structurally and functionally characterize these novel mutations to design novel drugs for improved clinical outcome. Hence, we used structural bioinformatics approaches i.e. molecular modeling, residues communication and molecular simulation to understand the impact of novel double S59Y-L85P, D86G-V180F and S104G-V130 M mutation on the structure, function of pncA encoded Pyrazinamidase (PZase) and resistance of Pyrazinamide (PZA). Our results revealed that these mutations alter the binding paradigm and destabilize the protein to release the drug. Protein commination network (PCN) revealed variations in the hub residues and sub-networks which consequently alter the internal communication and signaling. The region 1-75 demonstrated higher flexibility in the mutant structures and minimal by the wild type which destabilize of the internally arranged beta-sheets which consequently reduce the binding of PZA and potentially Fe ion in the mutants. Hydrogen bonding analysis further validated the findings. The total binding free energy (ΔG) for each complex i.e. wild type -7.46 kcal/mol, S59Y-L85P -5.21 kcal/mol, S104G-V130 M -5.33 kcal/mol while for the D86G-V180F mutant the TBE was calculated to be -6.26 kcal/mol. This further confirms that these mutations reduce the binding energy of PZA for PZase and causes resistance in the effective therapy for TB. The trajectories motion was also observed to be affected by these mutations. In conclusion, these mutations use destabilizing approach to reduce the binding of PZA and causes resistance. These features can be used to design novel structure-based drugs against Tuberculosis.

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

Unbiased molecular dynamics simulation of a first-in-class small molecule inhibitor binds to oncostatin M

Small molecule inhibitors (SMIs) targeting oncostatin M (OSM) signaling pathway represent new therapeutics to combat cancer, inflammatory bowel disease (IBD) and CNS disease. Recently, the first-in-class SMI named SMI-10B that target OSM and block its interaction with receptor (OSMR) were reported. However, the binding pocket and interaction mode of the compound on OSM remain poorly understood, which hampering the rational design of SMIs that target OSM. Here, using SMI-10B as a probe, the multiple pockets on OSM for small molecules binding were extensively explored by unbiased molecular dynamics (MD) simulations. Then, the near-native structure of the complex was identified by molecular mechanics generalized Born surface area (MM/GBSA) binding energy funnel. Moreover, the binding stabilities of the protein-ligand complexes in near- and non-native conformations were verified by additional independent MD runs and absolute free energy perturbation (FEP) calculation. In summary, the unique feature of SMI-10B spontaneously binds to OSM characterized here not only provide detailed information for understanding the molecular mechanism of SMI-10B binding to OSM, but also will facilitate the rational design of novel and more potent SMIs to block OSM signaling.

A systematic review of state-of-the-art strategies for machine learning-based protein function prediction

New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.

Mechanistic Insight into the Inhibition of Choline Acetyltransferase by Proton Pump Inhibitors

Various pharmacoepidemiological investigational studies have indicated that Proton Pump Inhibitors (PPIs) may increase the likelihood of developing Alzheimer's disease (AD) and non-AD related dementias. Previously, we have reported the inhibition of the acetylcholine biosynthesizing enzyme choline acetyltransferase (ChAT) by PPIs, for which omeprazole, lansoprazole, and pantoprazole exhibited IC₅₀ values of 0.1, 1.5, and 5.3 μM, respectively. In this study we utilize a battery of computational tools to perceive a mechanistic insight into the molecular interaction of PPIs with the ChAT binding pocket that may further help in designing novel ChAT ligands. Various in-silico tools make it possible for us to elucidate the binding interaction, conformational stability, and dynamics of the protein-ligand complexes within a 200 ns time frame. Further, the binding free energies for the PPI-ChAT complexes were explored. The results suggest that the PPIs exhibit equal or higher binding affinity toward the ChAT catalytic tunnel and are stable throughout the simulated time and that the pyridine ring of the PPIs interacts primarily with the catalytic residue His₃₂₄. A free energy landscape analysis showed that the folding process was linear, and the residue interaction network analysis can provide insight into the roles of various amino acid residues in stabilization of the PPIs in the ChAT binding pocket. As a major factor for the onset of Alzheimer's disease is linked to cholinergic dysfunction, our previous and the present findings give clear insight into the PPI interaction with ChAT. The scaffold can be further simplified to develop novel ChAT ligands, which can also be used as ChAT tracer probes for the diagnosis of cholinergic dysfunction and to initiate timely therapeutic interventions to prevent or delay the progression of AD.

Design, Synthesis, Antifungal Activity, and Molecular Docking of Streptochlorin Derivatives Containing the Nitrile Group

Based on the structures of natural products streptochlorin and pimprinine derived from marine or soil microorganisms, a series of streptochlorin derivatives containing the nitrile group were designed and synthesized through acylation and oxidative annulation. Evaluation for antifungal activity showed that compound 3a could be regarded as the most promising candidate-it demonstrated over 85% growth inhibition against Botrytis cinerea, Gibberella zeae, and Colletotrichum lagenarium, as well as a broad antifungal spectrum in primary screening at the concentration of 50 μg/mL. The SAR study revealed that non-substituent or alkyl substituent at the 2-position of oxazole ring were favorable for antifungal activity, while aryl and monosubstituted aryl were detrimental to activity. Molecular docking models indicated that 3a formed hydrogen bonds and hydrophobic interactions with Leucyl-tRNA Synthetase, offering a perspective for the possible mechanism of action for antifungal activity of the target compounds.

A systematic review on the state-of-the-art strategies for protein representation

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.

Atomic Mechanisms of Transthyretin Tetramer Dissociation Studied by Molecular Dynamics Simulations

The dissociation of the transthyretin (TTR) tetramer into a monomer is closely related to various TTR amyloidoses in humans. While the tetramer dissociation has been reported to be the rate-limiting step for TTR aggregation, few details are known about the mechanism. Here, molecular dynamics (MD) simulations were performed by combining conventional MD and biased metadynamics to investigate the mechanism for the wild-type (WT) and mutant (T119M) structures. Both were found to have a great deal in common. Conventional MD simulations reveal that interfacial hydrophobic interactions contribute significantly to stabilize the tetramer. Interfacial residues including L17, V20, L110, and V121 with close contacts form a hydrophobic channel. Metadynamics simulations indicate that the mouth opening of the hydrophobic channel is the first and the most difficult step for dissociation. Interactions of V20 between opposing dimers lock four monomers into the tetramer, and disruption of the interactions is found to be involved in the final step. During the dissociation, an increasing extent of solvation was observed by calculating the radial distribution functions of water around interfacial hydrophobic residues, suggesting that water plays a role in driving the tetramer dissociation. Moreover, compared to T119, residue M119 has a longer side chain that extends into the hydrophobic channel, making solvation more difficult, consistent with a higher energy barrier for dissociation of the T119M tetramer. This result provides a good explanation for the protective role of the T119M mutation. Overall, this study can provide atomic-level insights to better understand the pathogenesis of TTR amyloidosis and guide rational drug design in the future.

Evaluation of decursin and its isomer decursinol angelate as potential inhibitors of human glutamate dehydrogenase activity through in silico and enzymatic assay screening

Glutaminolysis is a typical hallmark of malignant tumors across different cancers. Glutamate dehydrogenase (GDH, GLUD1) is one such enzyme involved in the conversion of glutamate to α-ketoglutarate. High levels of GDH are associated with numerous diseases and is also a prognostic marker for predicting metastasis in colorectal cancer. Therefore, inhibiting GDH can be a crucial therapeutic target. Here in this study, we performed molecular docking analysis of 8 different plants derived single compounds collected from pubChem database for screening and selected decursin (DN) and decursinol angelate (DA). We performed molecular dynamics simulation (MD), monitored the stability, interaction for protein and docked ligand at 50 ns, and evaluated the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) free energy calculation on the twoselected compounds along with a standard inhibitor epigallocatechin gallate (EGCG) as reference. The final results showed the formation of stable hydrogen bond interactions by DN and DA in the residues of R400 and Y386 at the ADP activation site of GDH, which was important for the selective inhibition of GDH activity. Additionally, the total binding energy of DN and DA were -115.5 kJ/mol and -106.2 kJ/mol, which was higher than the standard reference GDH inhibitor EGCG (-92.8 kJ/mol). Furthermore, biochemical analysis for GDH inhibition substantiated our computational results and established DN and DA as novel GDH inhibitor. The percentage of IC₅₀ inhibition for DN and DA were 1.035 μM and 1.432 μM. Conclusively, DN and DA can be a novel therapeutic drug for inhibition of glutamate dehydrogenase.

LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data

Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

DRESIS: the first comprehensive landscape of drug resistance information

Widespread drug resistance has become the key issue in global healthcare. Extensive efforts have been made to reveal not only diverse diseases experiencing drug resistance, but also the six distinct types of molecular mechanisms underlying this resistance. A database that describes a comprehensive list of diseases with drug resistance (not just cancers/infections) and all types of resistance mechanisms is now urgently needed. However, no such database has been available to date. In this study, a comprehensive database describing drug resistance information named 'DRESIS' was therefore developed. It was introduced to (i) systematically provide, for the first time, all existing types of molecular mechanisms underlying drug resistance, (ii) extensively cover the widest range of diseases among all existing databases and (iii) explicitly describe the clinically/experimentally verified resistance data for the largest number of drugs. Since drug resistance has become an ever-increasing clinical issue, DRESIS is expected to have great implications for future new drug discovery and clinical treatment optimization. It is now publicly accessible without any login requirement at: https://idrblab.org/dresis/.

DrugMAP: molecular atlas and pharma-information of all drugs

The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.

Molecular dynamics simulations highlight the altered binding landscape at the spike-ACE2 interface between the Delta and Omicron variants compared to the SARS-CoV-2 original strain

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.529 variant (Omicron), represents a significant deviation in genetic makeup and function compared to previous variants. Following the BA.1 sublineage, the BA.2 and BA.3 Omicron subvariants became dominant, and currently the BA.4 and BA.5, which are quite distinct variants, have emerged. Using molecular dynamics simulations, we investigated the binding characteristics of the Delta and Omicron (BA.1) variants in comparison to wild-type (WT) at the interface of the spike protein receptor binding domain (RBD) and human angiotensin converting enzyme-2 (ACE2) ectodomain. The primary aim was to compare our molecular modelling systems with previously published observations, to determine the robustness of our approach for rapid prediction of emerging future variants. Delta and Omicron were found to bind to ACE2 with similar affinities (−39.4 and −43.3 kcal/mol, respectively) and stronger than WT (−33.5 kcal/mol). In line with previously published observations, the energy contributions of the non-mutated residues at the interface were largely retained between WT and the variants, with F456, F486, and Y489 having the strongest energy contributions to ACE2 binding. Further, residues N440K, Q498R, and N501Y were predicted to be energetically favourable in Omicron. In contrast to Omicron, which had the E484A and K417N mutations, intermolecular bonds were detected for the residue pairs E484:K31 and K417:D30 in WT and Delta, in accordance with previously published findings. Overall, our simplified molecular modelling approach represents a step towards predictive model systems for rapidly analysing arising variants of concern.

M6AREG: m6A-centered regulation of disease development and drug response

As the most prevalent internal modification in eukaryotic RNAs, N6-methyladenosine (m6A) has been discovered to play an essential role in cellular proliferation, metabolic homeostasis, embryonic development, etc. With the rapid accumulation of research interest in m6A, its crucial roles in the regulations of disease development and drug response are gaining more and more attention. Thus, a database offering such valuable data on m6A-centered regulation is greatly needed; however, no such database is as yet available. Herein, a new database named 'M6AREG' is developed to (i) systematically cover, for the first time, data on the effects of m6A-centered regulation on both disease development and drug response, (ii) explicitly describe the molecular mechanism underlying each type of regulation and (iii) fully reference the collected data by cross-linking to existing databases. Since the accumulated data are valuable for researchers in diverse disciplines (such as pathology and pathophysiology, clinical laboratory diagnostics, medicinal biochemistry and drug design), M6AREG is expected to have many implications for the future conduct of m6A-based regulation studies. It is currently accessible by all users at: https://idrblab.org/m6areg/.

A novel multi-class classification model for schizophrenia, bipolar disorder and healthy controls using comprehensive transcriptomic data

Two common psychiatric disorders, schizophrenia (SCZ) and bipolar disorder (BP), confer lifelong disability and collectively affect 2% of the world population. Because the diagnosis of psychiatry is based only on symptoms, developing more effective methods for the diagnosis of psychiatric disorders is a major international public health priority. Furthermore, SCZ and BP overlap considerably in terms of symptoms and risk genes. Therefore, the clarity of the underlying etiology and pathology remains lacking for these two disorders. Although many studies have been conducted, a classification model with higher accuracy and consistency was found to still be necessary for accurate diagnoses of SCZ and BP. In this study, a comprehensive dataset was combined from five independent transcriptomic studies. This dataset comprised 120 patients with SCZ, 101 patients with BP, and 149 healthy subjects. The partial least squares discriminant analysis (PLS-DA) method was applied to identify the gene signature among multiple groups, and 341 differentially expressed genes (DEGs) were identified. Then, the disease relevance of these DEGs was systematically performed, including (α) the great disease relevance of the identified signature, (β) the hub genes of the protein-protein interaction network playing a key role in psychiatric disorders, and (γ) gene ontology terms and enriched pathways playing a key role in psychiatric disorders. Finally, a popular multi-class classifier, support vector machine (SVM), was applied to construct a novel multi-class classification model using the identified signature for SCZ and BP. Using the independent test sets, the classification capacity of this multi-class model was assessed, which showed this model had a strong classification ability.

Investigating the structure-activity relationship of marine polycyclic batzelladine alkaloids as promising inhibitors for SARS-CoV-2 main protease (Mpro)

Over a span of two years ago, since the emergence of the first case of the novel coronavirus (SARS-CoV-2) in China, the pandemic has crossed borders causing serious health emergencies, immense economic crisis and impacting the daily life worldwide. Despite the discovery of numerous forms of precautionary vaccines along with other recently approved orally available drugs, yet effective antiviral therapeutics are necessarily needed to hunt this virus and its variants. Historically, naturally occurring chemicals have always been considered the primary source of beneficial medications. Considering the SARS-CoV-2 main protease (M^pro) as the duplicate key element of the viral cycle and its main target, in this paper, an extensive virtual screening for a focused chemical library of 15 batzelladine marine alkaloids, was virtually examined against SARS-CoV-2 main protease (M^pro) using an integrated set of modern computational tools including molecular docking (MDock), molecule dynamic (MD) simulations and structure-activity relationships (SARs) as well. The molecular docking predictions had disclosed four promising compounds including batzelladines H–I (8–9) and batzelladines F-G (6–7), respectively according to their prominent ligand-protein energy scores and relevant binding affinities with the (M^pro) pocket residues. The best two chemical hits, batzelladines H–I (8–9) were further investigated thermodynamically though studying their MD simulations at 100 ns, where they showed excellent stability within the accommodated (M^pro) pocket. Moreover, SARs studies imply the crucial roles of the fused tricyclic guanidinic moieties, its degree of unsaturation, position of the N–OH functionality and the length of the side chain as a spacer linking between two active sites, which disclosed fundamental structural and pharmacophoric features for efficient protein-ligand interaction. Such interesting findings are greatly highlighting further in vitro/vivo examinations regarding those marine natural products (MNPs) and their synthetic equivalents as promising antivirals.

Recognition of quinolone antibiotics by the multidrug efflux transporter MexB of Pseudomonas aeruginosa

The drug/proton antiporter MexB is the engine of the major efflux pump MexAB-OprM in Pseudomonas aeruginosa. This protein is known to transport a large variety of compounds, including antibiotics, thus conferring a multi-drug resistance phenotype. Due to the difficulty of producing co-crystals, only two X-ray structures of MexB in a complex with ligands are available to date, and mechanistic aspects are largely hypothesized based on the body of data collected for the homologous protein AcrB of Escherichia coli. In particular, a recent study (Ornik-Cha, Wilhelm, Kobylka et al., Nat. Commun., 2021, 12, 6919) reported a co-crystal structure of AcrB in a complex with levofloxacin, an antibiotic belonging to the important class of (fluoro)-quinolones. In this work, we performed a systematic ensemble docking campaign coupled to the cluster analysis and molecular-mechanics optimization of docking poses to study the interaction between 36 quinolone antibiotics and MexB. We additionally investigated surface complementarity between each molecule and the transporter and thoroughly assessed the computational protocol adopted against the known experimental data. Our study reveals different binding preferences of the investigated compounds towards the sub-sites of the large deep binding pocket of MexB, supporting the hypothesis that MexB substrates oscillate between different binding modes with similar affinity. Interestingly, small changes in the molecular structure translate into significant differences in MexB–quinolone interactions. All the predicted binding modes are available for download and visualization at the following link: https://www.dsf.unica.it/dock/mexb/quinolones.

Putative binding modes (BMs) of quinolones to the bacterial efflux transporter MexB were identified. Multiple interaction patterns are possible, supporting the hypothesis that substrates oscillate between different BMs with similar affinity.

Exploration and validation of Taraxacum mongolicum anti-cancer effect

Taraxacum mongolicum gained a lot of concern and was applied in 93 formulas in China due to its fame as a traditional Chinese medicine. The earliest recorded application of Taraxacum mongolicum was traced back to the Han dynasty. Generations of doctors boosted the usage and enriched the pharmacological mechanism. Clinical application of the Taraxacum mongolicum is flourishing as it treats multiple diseases. This study aims to explore the anti-cancer effect, retrieve the active ingredients and screen the key targets of Taraxacum mongolicum in cancer therapy. We collected and evaluated 10 key active compounds to investigate the anti-cancer effect via 69 significant targets and a variety of biological processes and pathways. Gene Ontology (GO) enrichment analysis uncovered targets associated with protein phosphorylation, cell proliferation and apoptotic processes via regulation of kinases, ATP and enzyme binding activities. Half of the top 20 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were directly involved in cancer. Based on standard selection criteria, seven hub targets were obtained. These targets functioned through distinct patterns and pathways in realizing the anti-cancer effect. Molecular docking was conducted to validate the potential combination between compounds and hub targets to explore the pharmacological mechanism of key compounds in Taraxacum mongolicum against cancer. In summary, our findings indicate that the famous and widely used Chinese herb, Taraxacum mongolicum, shows good anti-cancer effect through its active compounds, targeted genes, and multiple involved biological processes. The results may provide a theoretical basis for subsequent experimental validation and drug development of Taraxacum mongolicum extract against cancer.

Investigation of the binding and dynamic features of A.30 variant revealed higher binding of RBD for hACE2 and escapes the neutralizing antibody: A molecular simulation approach

With the emergence of Delta and Omicron variants, many other important variants of SARS-CoV-2, which cause Coronavirus disease-2019, including A.30, are reported to increase the concern created by the global pandemic. The A.30 variant, reported in Tanzania and other countries, harbors spike gene mutations that help this strain to bind more robustly and to escape neutralizing antibodies. The present study uses molecular modelling and simulation-based approaches to investigate the key features of this strain that result in greater infectivity. The protein-protein docking results for the spike protein demonstrated that additional interactions, particularly two salt-bridges formed by the mutated residue Lys484, increase binding affinity, while the loss of key residues at the N terminal domain (NTD) result in a change to binding conformation with monoclonal antibodies, thus escaping their neutralizing effects. Moreover, we deeply studied the atomic features of these binding complexes through molecular simulation, which revealed differential dynamics when compared to wild type. Analysis of the binding free energy using MM/GBSA revealed that the total binding free energy (TBE) for the wild type receptor-binding domain (RBD) complex was -58.25 kcal/mol in contrast to the A.30 RBD complex, which reported -65.59 kcal/mol. The higher TBE for the A.30 RBD complex signifies a more robust interaction between A.30 variant RBD with ACE2 than the wild type, allowing the variant to bind and spread more promptly. The BFE for the wild type NTD complex was calculated to be -65.76 kcal/mol, while the A.30 NTD complex was estimated to be -49.35 kcal/mol. This shows the impact of the reported substitutions and deletions in the NTD of A.30 variant, which consequently reduce the binding of mAb, allowing it to evade the immune response of the host. The reported results will aid the development of cross-protective drugs against SARS-CoV-2 and its variants.