Software for molecular docking: a review

The Potential of Molecular Docking for Predictive Toxicology

Molecular modeling techniques are widely used in medicinal chemistry for the study of biological targets, the rational design of new drugs, or the investigation of their mechanism of action.They are also applied in toxicology to identify chemical potential harmful effects.Molecular docking is a computational technique to predict the ligand binding mode and evaluate the interaction energy with a biological target.This chapter describes a computational workflow to predict possible endocrine disruptors on peroxisome proliferator-activated receptor alpha (PPARα), a nuclear receptor involved in glucose and lipid metabolism. The analyzed compounds are food contact chemicals, natural or synthetic substances intentionally added to food or released from the package or during production or technological processes.

Truncations and in silico docking to enhance the analytical response of aptamer-based biosensors

Aptamers are short oligonucleotides capable of binding specifically to various targets (i.e., small molecules, proteins, and whole cells) which have been introduced in biosensors such as in the electrochemical aptamer-based (E-AB) sensing platform. E-AB sensors are comprised of a redox-reporter-modified aptamer attached to an electrode that undergoes, upon target addition, a binding-induced change in electron transfer rates. To date, E-AB sensors have faced a limitation in the translatability of aptamers into the sensing platform presumably because sequences obtained from Systematic Evolution of Ligands by Exponential Enrichment (SELEX) are typically long (>80 nucleotides) and that obtaining structural information remains time and resource consuming. In response, we explore the utility of aptamer base truncations and in silico docking to improve their translatability into E-AB sensors. Here, we first apply this to the glucose aptamer, which we characterize in solution using NMR methods to guide design and translate truncated variants in E-AB biosensors. We further investigated the applicability of the truncation and computational approaches to four other aptamer systems (vancomycin, cocaine, methotrexate and theophylline) from which we derived functional E-AB sensors. We foresee that our strategy will increase the success rate of translating aptamers into sensing platforms to afford low-cost measurements of molecules directly in undiluted complex matrices.

Safranal alleviates pentetrazole-induced epileptic seizures in mice by inhibiting the NF-κB signaling pathway and mitochondrial-dependent apoptosis through GSK-3β inactivation

ETHNOPHARMACOLOGICAL RELEVANCE

Saffron, a traditional Chinese medicine, is derived from Crocus sativus L. stigmas and has been reported to possess neuroprotective properties and potentially contribute to the inhibition of apoptosis and inflammation. Safranal, a potent monothyral aldehyde, is a main component of saffron that has been reported to have antiepileptic activity. However, the specific mechanism by which safranal suppresses epileptic seizures via its antiapoptotic and anti-inflammatory properties is unclear.

AIM

To evaluate the effect of safranal on seizure severity, inflammation, and postictal neuronal apoptosis in a mouse model of pentetrazole (PTZ)-induced seizures and explore the underlying mechanism involved.

MATERIALS AND METHODS

The seizure stage and latency of stage 2 and 4 were quantified to assess the efficacy of safranal in mitigating PTZ-induced epileptic seizures in mice. Electroencephalography (EEG) was employed to monitor epileptiform afterdischarges in each experimental group. The cognitive abilities and motor functions of the mice were evaluated using the novel object recognition test and the open field test, respectively. Neurons were quantified using hematoxylin and eosin staining. Additionally, bioinformatics tools were utilized to predict the interactions between safranal and specific target proteins. Glycogen synthase kinase-3β (GSK-3β), mitochondrial apoptosis-related proteins, and inflammatory factor levels were analyzed through western blotting. Tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) concentrations in brain tissue were assessed by ELISA.

RESULTS

Safranal decreased the average seizure stage and increased the lantency of stage 2 and 4 seizures in PTZ-induced epileptic mice. Additionally, safranal exhibited neuroprotective effects on hippocampal CA1 and CA3 neurons and reduced hyperactivity caused by postictal hyperexcitability. Bioinformatics analysis revealed that safranal can bind to five specific proteins, including GSK-3β. By promoting Ser9 phosphorylation and inhibiting GSK-3β activity, safranal effectively suppressed the NF-κB signaling pathway. Moreover, the findings indicate that safranal treatment can decrease TNF-α and IL-1β levels in the cerebral tissues of epileptic mice and downregulate mitochondrial apoptosis-related proteins, including Bcl-2, Bax, Bak, Caspase 9, and Caspase 3.

CONCLUSION

Safranal can suppress the NF-κB signaling pathway and mitochondrial-dependent apoptosis through GSK-3β inactivation, suggesting that it is a promising therapeutic agent for epilepsy treatment.

Study on the Inhibitory Effect of Bioactive Peptides Derived from Yak Milk Cheese on Cholesterol Esterase

The bioactive peptides derived from yak milk cheese exhibited cholesterol-lowering properties. However, there was limited research on their inhibitory effects on cholesterol esterase (CE) and elucidation of their potential inhibitory mechanisms. In this study, we identified CE-inhibiting peptides through virtual screening and in vitro assays. Additionally, molecular docking and molecular dynamics studies were conducted to explore the mechanisms. The results indicated that peptides RK7 (RPKHPIK), KQ7 (KVLPVPQ), QP13 (QEPVLGPVRGPFP), TL9 (TPVVVPPFL), VN10 (VYPFPGPIPN), LQ10 (LPPTVMFPPQ), and SN12 (SLVYPFPGPIPN) possessed molecular weights of less than 1.5 kDa and a high proportion of hydrophobic amino acids, demonstrating notable inhibitory effects on CE. Molecular docking and dynamics revealed that peptides RK7, KQ7, QP13, and VN10 bound to key amino acid residues Arg423, His435, and Ser422 of CE through hydrogen bonds, hydrophobic interactions, salt bridges, and π-π stacking, occupying the substrate-binding site and exerting inhibitory effects on CE. The four peptides were further synthesized to verify their CE-inhibitory effects in vitro. RK7, KQ7, QP13, and VN10 exhibited inhibitory activity on CE with IC50 values of 8.16 × 10-7 mol/L, 8.10 × 10-7 mol/L, 4.63 × 10-7 mol/L, and 7.97 × 10-7 mol/L; RK7, KQ7, QP13, and VN10 were effective in inhibiting CE after simulated gastrointestinal digestion, especially with a significant increase in the inhibitory activity of KQ7 and RK7, respectively. Our findings suggested that bioactive peptides from yak milk cheese represented a novel class of potential CE inhibitors.

Exploring the neuroprotective potential of KC14 peptide from Cyprinus carpio against oxidative stress-induced neurodegeneration by regulating antioxidant mechanism

BACKGROUND

Oxidative stress, a condition characterized by excessive production of reactive oxygen species (ROS), can cause significant damage to cellular macromolecules, leading to neurodegeneration. This underscores the need for effective antioxidant therapies that can mitigate oxidative stress and its associated neurodegenerative effects. KC14 peptide derived from liver-expressed antimicrobial peptide-2 A (LEAP 2 A) from Cyprinus carpio L. has been identified as a potential therapeutic agent. This study focuses on the antioxidant and neuroprotective properties of the KC14 peptide is to evaluate its effectiveness against oxidative stress and neurodegeneration.

METHODS

The antioxidant capabilities of KC14 were initially assessed through in silico docking studies, which predicted its potential to interact with oxidative stress-related targets. Subsequently, the peptide was tested at concentrations ranging from 5 to 45 µM in both in vitro and in vivo experiments. In vivo studies involved treating H2O2-induced zebrafish larvae with KC14 peptide to analyze its effects on oxidative stress and neuroprotection.

RESULTS

KC14 peptide showed a protective effect against the developmental malformations caused by H2O2 stress, restored antioxidant enzyme activity, reduced neuronal damage, and lowered lipid peroxidation and nitric oxide levels in H2O2-induced larvae. It enhanced acetylcholinesterase activity and significantly reduced intracellular ROS levels (p < 0.05) dose-dependently. Gene expression studies showed up-regulation of antioxidant genes with KC14 treatment under H2O2 stress.

CONCLUSIONS

This study highlights the potent antioxidant activity of KC14 and its ability to confer neuroprotection against oxidative stress can provide a novel therapeutic agent for combating neurodegenerative diseases induced by oxidative stress.

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.

PIDiff: Physics informed diffusion model for protein pocket-specific 3D molecular generation

Designing drugs capable of binding to the structure of target proteins for treating diseases is essential in drug development. Recent remarkable advancements in geometric deep learning have led to unprecedented progress in three-dimensional (3D) generation of ligands that can bind to the protein pocket. However, most existing methods primarily focus on modeling the geometric information of ligands in 3D space. Consequently, these methods fail to consider that the binding of proteins and ligands is a phenomenon driven by intrinsic physicochemical principles. Motivated by this understanding, we propose PIDiff, a model for generating molecules by accounting in the physicochemical principles of protein-ligand binding. Our model learns not only the structural information of proteins and ligands but also to minimize the binding free energy between them. To evaluate the proposed model, we introduce an experimental framework that surpasses traditional assessment methods by encompassing various essential aspects for the practical application of generative models to actual drug development. The results confirm that our model outperforms baseline models on the CrossDocked2020 benchmark dataset, demonstrating its superiority. Through diverse experiments, we have illustrated the promising potential of the proposed model in practical drug development.

Evaluation of the Binding Properties of A New Phenylurea Appended Carbazole Compound to Pepsin/Trypsin by Computational and Multi-Spectral Analysis

A novel carbazole compound, named 1-(9-ethyl-9H-carbazol-3-yl)-3-phenylurea (Cpu) was synthesized and its binding properties with protease enzymes (pepsin and trypsin) has been examined by steady-state fluorescence measurements, UV/vis absorption, infrared (FT-IR) and circular dicroism (CD) spectroscopies and also computational methods. The fluorescence experimental results indicated that the quenching mechanism of enzyme by Cpu is static process. The thermodynamic parameters (both negative ΔH/ΔS) and molecular docking results suggested that the binding of Cpu to pepsin/trypsin were driven by hydrogen bonds and van der Waals forces. Based on Förster's theory, the binding distance (r) between pepsin/trypsin and Cpu was calculated to be 3.072/2.784 nm, which implies that non-radiative energy transfer occurs from enzyme to Cpu. Furthermore, absorption, CD, and FT-IR spectral analysis provided an evidence that the presence of Cpu induced notable changes in the secondary structures and microenvironmental of both pepsin and trypsin, supporting its significant influence on these enzymes.

Application of artificial intelligence in drug design: A review

Artificial intelligence (AI) is a field of computer science that involves acquiring information, developing rule bases, and mimicking human behaviour. The fundamental concept behind AI is to create intelligent computer systems that can operate with minimal human intervention or without any intervention at all. These rule-based systems are developed using various machine learning and deep learning models, enabling them to solve complex problems. AI is integrated with these models to learn, understand, and analyse provided data. The rapid advancement of Artificial Intelligence (AI) is reshaping numerous industries, with the pharmaceutical sector experiencing a notable transformation. AI is increasingly being employed to automate, optimize, and personalize various facets of the pharmaceutical industry, particularly in pharmacological research. Traditional drug development methods areknown for being time-consuming, expensive, and less efficient, often taking around a decade and costing billions of dollars. The integration of artificial intelligence (AI) techniques addresses these challenges by enabling the examination of compounds with desired properties from a vast pool of input drugs. Furthermore, it plays a crucial role in drug screening by predicting toxicity, bioactivity, ADME properties (absorption, distribution, metabolism, and excretion), physicochemical properties, and more. AI enhances the drug design process by improving the efficiency and accuracy of predicting drug behaviour, interactions, and properties. These approaches further significantly improve the precision of drug discovery processes and decrease clinical trial costs leading to the development of more effective drugs.

CAPRI-Q: The CAPRI resource evaluating the quality of predicted structures of protein complexes

Protein interactions are essential for cellular processes. In recent years there has been significant progress in computational prediction of 3D structures of individual protein chains, with the best-performing algorithms reaching sub-Ångström accuracy. These techniques are now finding their way into the prediction of protein interactions, adding to the existing modeling approaches. The community-wide Critical Assessment of Predicted Interactions (CAPRI) has been a catalyst for the development of procedures for the structural modeling of protein assemblies by organizing blind prediction experiments. The predicted structures are assessed against unpublished experimentally determined structures using a set of metrics with proven robustness that have been established in the CAPRI community. In addition, several advanced benchmarking databases provide targets against which users can test docking and assembly modeling software. These include the Protein-Protein Docking Benchmark, the CAPRI Scoreset, and the Dockground database, all developed by members of the CAPRI community. Here we present CAPRI-Q, a stand-alone model quality assessment tool, which can be freely downloaded or used via a publicly available web server. This tool applies the CAPRI metrics to assess the quality of query structures against given target structures, along with other popular quality metrics such as DockQ, TM-score and l-DDT, and classifies the models according to the CAPRI model quality criteria. The tool can handle a variety of protein complex types including those involving peptides, nucleic acids, and oligosaccharides. The source code is freely available from https://gitlab.in2p3.fr/cmsb-public/CAPRI-Q and its web interface through the Dockground resource at https://dockground.compbio.ku.edu/assessment/.

Insilico assessment of hesperidin on SARS-CoV-2 main protease and RNA polymerase: Molecular docking and dynamics simulation approach

The present study uses molecular docking and dynamic simulations to evaluate the inhibitory effect of flavonoid glycosides-based compounds on coronavirus Main protease (Mpro) and RNA polymerase. The Molegro Virtual Docker (MVD) software is utilized to simulate and calculate the binding parameters of compounds with coronavirus. The docking results show that the selected herbal compounds are more effective than those of chemical compounds. It is also revealed that five herbal ligands and two chemical ligands have the best docking scores. Furthermore, a Molecular Dynamics (MD) simulation was conducted for Hesperidin, confirming docking results. Analysis based on different parameters such as Root-mean-square deviation (RMSD), Root mean square fluctuation (RMSF), Radius of gyration (Rg), Solvent accessibility surface area (SASA), and the total number of hydrogen bonds suggests that Hesperidin formed a stable complex with Mpro. Absorption, Distribution, Metabolism, Excretion, And Toxicity (ADMET) analysis was performed to compare Hesperidin and Grazoprevir as potential antiviral medicines, evaluating both herbal and chemical ligand results. According to the study, herbal compounds could be effective on coronavirus and are admissible candidates for developing potential operative anti-viral medicines. Hesperidin was found to be the most acceptable interaction. Grazoprevir is an encouraging candidate for drug development and clinical trials, with the potential to become a highly effective Mpro inhibitor. Compared to RNA polymerase, Mpro showed a greater affinity for bonding with Hesperidin. van der Waals and electrostatic energies dominated, creating a stable Hesperidin-Mpro and Hesperidin-RNA polymerase complex.

FitScore: a fast machine learning-based score for 3D virtual screening enrichment

Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large databases is possible with cloud-scale computing. However, rapid docking necessitates compromises in scoring, often leading to poor enrichment and an abundance of false positives in docking results. This work describes a new scoring function composed of two parts - a knowledge-based component that predicts the probability of a particular atom type being in a particular receptor environment, and a tunable weight matrix that converts the probability predictions into a dimensionless score suitable for virtual screening enrichment. This score, the FitScore, represents the compatibility between the ligand and the binding site and is capable of a high degree of enrichment across standardized docking test sets.

Virtual Screening and Validation of Affinity DNA Functional Ligands for IgG Fc Segment

The effective attachment of antibodies to the immune sensing interface is a crucial factor that determines the detection performance of immunosensors. Therefore, this study aims to investigate a novel antibody immobilization material with low molecular weight, high stability, and excellent directional immobilization effect. In this study, we employed molecular docking technology based on the ZDOCK algorithm to virtually screen DNA functional ligands (DNAFL) for the Fc segment of antibodies. Through a comprehensive analysis of the key binding sites and contact propensities at the interface between DNAFL and IgG antibody, we have gained valuable insights into the affinity relationship, as well as the principles governing amino acid and nucleotide interactions at this interface. Furthermore, molecular affinity experiments and competitive binding experiments were conducted to validate both the binding ability of DNAFL to IgG antibody and its actual binding site. Through affinity experiments using multi-base sequences, we identified bases that significantly influence antibody-DNAFL binding and successfully obtained DNAFL with an enhanced affinity towards the IgG Fc segment. These findings provide a theoretical foundation for the targeted design of higher-affinity DNAFLs while also presenting a new technical approach for immunosensor preparation with potential applications in biodetection.

Molecular interaction analysis of ferulic acid (4-hydroxy-3-methoxycinnamic acid) as main bioactive compound from palm oil waste against MCF-7 receptors: An in silico study

Ferulic acid (4-hydroxy-3-methoxycinnamic acid) is a phytochemical compound that is commonly found in conjugated forms within mono-, di-, polysaccharides and other organic compounds in cell walls of grain, fruits, and vegetables. This compound is highly abundant in the palm oil waste. The aim of the study was to predict the anticancer activity of ferulic acid against the breast cancer cell lines (MCF-7) receptors through a computational analysis. MCF-7 receptors with PDB IDs of 1R5K, 2IOG, 4IV2, 4IW6, 5DUE, 5T92, and 5U2B were selected based on the Simplified Molecular Input Line Entry System (SMILES) similarity of the native ligand. Thereafter, the protein was prepared on Chimera 1.16 and docked with ferulic acid on Autodock Vina 1.2.5. The ligand-protein complex interaction was validated by computing the root mean square fluctuation (RMSF) and radius of gyration (Rg) through molecular dynamic simulation. In addition, an absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction was performed on ferulic acid using the pkCSM platform. The molecular docking revealed that the ferulic acid could interact with all receptors as indicated by the affinity energy <-5 kcal/mol. The compound had the most optimum interaction with receptor 2IOG (affinity energy=-6.96 kcal/mol), involving hydrophobic interaction (n=12) and polar hydrogen interaction (n=4). The molecular dynamic simulation revealed that the complex had an RMSF of 1.713 Å with a fluctuation of Rg value around 1.000 Å. The ADMET properties of ferulic acid suggested that the compound is an ideal drug candidate. In conclusion, this study suggested that ferulic acid, which can be isolated from palm oil waste, has the potential to interact with MCF-7 receptors.

Exploring the Potential of Oleanolic Acid Dimers-Cytostatic and Antioxidant Activities, Molecular Docking, and ADMETox Profile

The presented work aimed to explore the potential of oleanolic acid dimers (OADs): their cytostatic and antioxidant activities, molecular docking, pharmacokinetics, and ADMETox profile. The cytostatic properties of oleanolic acid (1) and its 14 synthesised dimers (2a-2n) were evaluated against 10 tumour types and expressed as IC50 values. Molecular docking was performed with the CB-Dock2 server. Antioxidant properties were evaluated with the CUPRAC method. ADMETox properties were evaluated with the ADMETlab Manual (2.0) database. The results indicate that the obtained OADs can be effective cytostatic agents, for which the IC50 not exceeded 10.00 for many tested cancer cell lines. All OADs were much more active against all cell lines than the mother compound (1). All dimers can inhibit the interaction between the 1MP8 protein and cellular proteins with the best results for compounds 2f and 2g with unsaturated bonds within the linker. An additional advantage of the tested OADs was a high level of antioxidant activity, with Trolox equivalent for OADs 2c, 2d, 2g-2j, 2l, and 2m of approximately 0.04 mg/mL, and beneficial pharmacokinetics and ADMETox properties. The differences in the DPPH and CUPRAC assay results obtained for OADs may indicate that these compounds may be effective antioxidants against different radicals.

Novel drug design and repurposing: An opportunity to improve translational research in cardiovascular diseases?

Drug repurposing is defined as the use of approved therapeutic drugs for indications different from those for which they were originally designed. Repositioning diminishes both the time and cost for drug development by omitting the discovery stage, the analysis of absorption, distribution, metabolism, and excretion routes, as well as the studies of the biochemical and physiological effects of a new compound. Besides, drug repurposing takes advantage of the increased bioinformatics knowledge and availability of big data biology. There are many examples of drugs with repurposed indications evaluated in in vitro studies, and in pharmacological, preclinical, or retrospective clinical analyses. Here, we briefly review some of the experimental strategies and technical advances that may improve translational research in cardiovascular diseases. We also describe exhaustive research from basic science to clinical studies that culminated in the final approval of new drugs and provide examples of successful drug repurposing in the field of cardiology.

Quantum Molecular Docking with a Quantum-Inspired Algorithm

Molecular docking (MD) is a crucial task in drug design, which predicts the position, orientation, and conformation of the ligand when it is bound to a target protein. It can be interpreted as a combinatorial optimization problem, where quantum annealing (QA) has shown a promising advantage for solving combinatorial optimization. In this work, we propose a novel quantum molecular docking (QMD) approach based on a QA-inspired algorithm. We construct two binary encoding methods to efficiently discretize the degrees of freedom with an exponentially reduced number of bits and propose a smoothing filter to rescale the rugged objective function. We propose a new quantum-inspired algorithm, hopscotch simulated bifurcation (hSB), showing great advantages in optimizing over extremely rugged energy landscapes. This hSB can be applied to any formulation of an objective function under binary variables. An adaptive local continuous search is also introduced for further optimization of the discretized solution from hSB. Concerning the stability of docking, we propose a perturbation detection method to help rank the candidate poses. We demonstrate our approach on a typical data set. QMD has shown advantages over the search-based Autodock Vina and the deep-learning DIFFDOCK in both redocking and self-docking scenarios. These results indicate that quantum-inspired algorithms can be applied to solve practical problems in drug discovery even before quantum hardware become mature.

Advances in Parkinson's disease research - A computational network pharmacological approach

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, is projected to see a significant rise in incidence over the next three decades. The precise treatment of PD remains a formidable challenge, prompting ongoing research into early diagnostic methodologies. Network pharmacology, a burgeoning field grounded in systems biology, examines the intricate networks of biological systems to identify critical signal nodes, facilitating the development of multi-target therapeutic molecules. This approach systematically maps the components of Parkinson's disease, thereby reducing its complexity. In this review, we explore the application of network pharmacology workflows in PD, discuss the techniques employed in this field, and evaluate the current advancements and status of network pharmacology in the context of Parkinson's disease. The comprehensive insights will pave newer paths to explore early disease biomarkers and to develop diagnosis with a holistic in silico, in vitro, in vivo and clinical studies.

Hybrid Virtual Screening Approach to Predict Novel Natural Compounds against HIV-1 CCR5

BACKGROUND

Human immunodeficiency virus (HIV) infection continues to pose a major global health challenge. HIV entry into host cells via membrane fusion mediated by the viral envelope glycoprotein gp120/gp41 is a key step in the HIV life cycle. CCR5, expressed on CD4+ T cells and macrophages, acts as a coreceptor facilitating HIV-1 entry. The CCR5 antagonist maraviroc is used to treat HIV infection. However, it can cause adverse effects and has limitations such as only inhibiting CCR5-tropic viruses. There remains a need to develop alternative CCR5 inhibitors with improved safety profiles.

PROBLEM STATEMENT

Natural products may offer advantages over synthetic inhibitors including higher bioavailability, binding affinity, effectiveness, lower toxicity, and molecular diversity. However, screening the vast chemical space of natural compounds to identify novel CCR5 inhibitors presents challenges. This study aimed to address this gap through a hybrid ligand-based pharmacophore modeling and molecular docking approach to virtually screen large natural product databases.

METHODS

A reliable pharmacophore model was developed based on 311 known CCR5 antagonists and validated against an external data set. Five natural product databases containing over 306,000 compounds were filtered based on drug-likeness rules. The validated pharmacophore model screened the databases to identify 611 hits. Key residues of the CCR5 receptor crystal structure were identified for docking. The top hits were docked, and interactions were analyzed. Molecular dynamics simulations were conducted to examine complex stability. Computational prediction evaluated pharmacokinetic properties.

RESULTS

Three compounds exhibited similar interactions and binding energies to maraviroc. MD simulations demonstrated complex stability comparable to maraviroc. One compound showed optimal predicted absorption, minimal metabolism, and a lower likelihood of interactions than maraviroc.

CONCLUSION

This computational screening workflow identified three natural compounds with promising CCR5 inhibition and favorable pharmacokinetic profiles. One compound emerged as a lead based on bioavailability potential and minimal interaction risk. These findings present opportunities for developing alternative CCR5 antagonists and warrant further experimental investigation. Overall, the hybrid virtual screening approach proved effective for mining large natural product spaces to discover novel molecular entities with drug-like properties.

Link prediction in protein-protein interaction network: A similarity multiplied similarity algorithm with paths of length three

Protein-protein interactions (PPIs) are crucial for various biological processes, and predicting PPIs is a major challenge. To solve this issue, the most common method is link prediction. Currently, the link prediction methods based on network Paths of Length Three (L3) have been proven to be highly effective. In this paper, we propose a novel link prediction algorithm, named SMS, which is based on L3 and protein similarities. We first design a mixed similarity that combines the topological structure and attribute features of nodes. Then, we compute the predicted value by summing the product of all similarities along the L3. Furthermore, we propose the Max Similarity Multiplied Similarity (maxSMS) algorithm from the perspective of maximum impact. Our computational prediction results show that on six datasets, including S. cerevisiae, H. sapiens, and others, the maxSMS algorithm improves the precision of the top 500, area under the precision-recall curve, and normalized discounted cumulative gain by an average of 26.99%, 53.67%, and 6.7%, respectively, compared to other optimal methods.

Manganese(II) and Zinc(II) metal complexes of novel bidentate formamide-based Schiff base ligand: synthesis, structural characterization, antioxidant, antibacterial, and in-silico molecular docking study

A new bidentate Schiff base ligand (C16H16Cl2N4), condensation product of ethylene diamine and 4-chloro N-phenyl formamide, and its metal complexes [M(C16H16Cl2N4)2(OAc)2] (where M = Mn(II) and Zn(II)) were synthesized and characterized using various analytical and spectral techniques, including high-resolution mass spectrometry (HRMS), elemental analysis, ultraviolet-visible (UV-vis), Fourier-transform infrared (FTIR) spectroscopy, AAS, molar conductance, 1H NMR, and powder XRD. All the compounds were non-electrolytes and nanocrystalline. The synthesized compounds were assessed for antioxidant potential by DPPH radical scavenging and FRAP assay, with BHT serving as the positive control. Inhibitory concentration at 50% inhibition (IC50) values were calculated and used for comparative analysis. Furthermore, the prepared compounds were screened for antibacterial activity against two Gram-negative bacteria (Staphylococcus aureus and Bacillus subtilis) and two Gram-positive bacteria (Escherichia coli and Salmonella typhi) using disk-diffusion methods, with amikacin employed as the standard reference. The comparison of inhibition zones revealed that the complexes showed better antibacterial activity than the ligand. To gain insights into the molecular interactions underlying the antibacterial activity, the ligand and complexes were analyzed for their binding affinity with S. aureus tyrosyl-tRNA synthetase (PDB ID: 1JIL) and S. typhi cell membrane protein OmpF complex (PDB ID: 4KR4). These analyses revealed robust interactions, validating the observed antibacterial effects against the tested bacterial strains.

Insight into the inhibitory potential of metal complexes supported by (E)-2-morpholino-N-(thiophen-2-ylmethylene)ethanamine: synthesis, structural properties, biological evaluation and docking studies

A thiophene-derived Schiff base ligand (E)-2-morpholino-N-(thiophen-2-ylmethylene)ethanamine was used for the synthesis of M(II) complexes, [TEM(M)X2] (M = Co, Cu, Zn; X = Cl; M = Cd, X = Br). Structural characterization of the synthesized complexes revealed distorted tetrahedral geometry around the M(II) center. In vitro investigation of the synthesized ligand and its M(II) complexes showed considerable anti-urease and leishmanicidal potential. The synthesized complexes also exhibited a significant inhibitory effect on urease, with IC50 values in the range of 3.50-8.05 μM. In addition, the docking results were consistent with the experimental results. A preliminary study of human colorectal cancer (HCT), hepatic cancer (HepG2), and breast cancer (MCF-7) cell lines showed marked anticancer activities of these complexes.

Exploring the mechanism of Si-Miao-Yong-An decoction on heart failure based on molecular docking and network pharmacology

The SwissTargetPrediction was employed to predict the potential drug targets of the active component of Si-Miao-Yong-An decoction (SMYAD). The therapeutic targets for HF were searched in the Genecard database, and Cytoscape3.9.1 software was used to construct the "drug-component-target-disease network" diagram. In addition, the String platform was used to construct Protein-Protein Interaction (PPI) network, and the DAVID database was used for GO and KEGG analysis. AutoDockTools-1.5.6 software was used for molecular docking verification. Network pharmacology studies have shown that AKT 1, ALB, and CASP 3 are the key targets of action of SMYAD against heart failure. The active compounds are quercetin and kaempferol.

Fluorinated benzoxazinones designed via MIA-QSAR, docking and molecular dynamics as protoporphyrinogen IX oxidase inhibitors

BACKGROUND

Fluorine plays a significant role in agrochemical science because approximately 25% of herbicides licensed worldwide contain this element. In a pool of previously synthesized benzoxazinones, some compounds contained fluorine and demonstrated inhibitory activities against protoporphyrinogen IX oxidase (PPO). Therefore, three data sets of benzoxazinone derivatives with known inhibitory activity against PPO were employed to build a multivariate image analysis applied to a quantitative structure-activity relationships (MIA-QSAR) model to identify improved analogs with at least one fluorine substituent.

RESULTS

The QSAR model was vigorously validated and demonstrated to be highly predictive (r2 = 0.85, q2 = 0.71, and r2 pred = 0.88); thus, the model can provide reliable estimations for the PPO inhibitory activity of unknown derivatives. From these compounds, a couple of N-substituted benzoxazinones that contained the -CH2CHF2 group were found with predicted pKi values larger than 8 (Ki in mol L-1) and higher lipophilicity than the most active data set compounds. In addition, we carried out a systematic investigation of the binding mode of PPO by performing computational docking followed by molecular dynamics simulations. The proposed binding mode was consistent with experimental studies, and several potential key residues were identified.

CONCLUSION

Two new proposed benzoxazinones exhibited better performance than compounds of the data set, and fluorine substituents played pivotal roles in describing the biological activities. © 2024 Society of Chemical Industry.

Efficient molecular conformation generation with quantum-inspired algorithm

CONTEXT

Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5Å), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware becomes mature.

METHODS

The objective function of MU is defined as the sum of all internal distances between atoms in the molecule, which is a high-order unconstrained binary optimization (HUBO) problem. The degree of freedom of variables is discretized and encoded with binary variables by the phase encoding method. We employ the quantum-inspired simulated bifurcation algorithm for optimization. The public QM9 dataset is generated by DFT. The simulation experiment of quantum computation is implemented by MindQuantum using QAOA.

Delineating the Antiapoptotic Property of Apigenin as an Antitumor Agent: A Computational and In Vitro Study on HeLa Cells

Apigenin, a flavonoid, is reported to have multiple health benefits including cancer prevention; this study evaluates the drug likeliness and Swiss ADME properties of apigenin. Apoptosis, which is a key hallmark of cancer, is associated with the deregulation of the balance between proapoptotic proteins and antiapoptotic proteins such as BCL-2,BCL-xl, BFL-1, BCL-w, BRAG-16, and MCL-1. The docking studies of apigenin with the mentioned proteins was performed to identify the interactions between the ligand and proteins, which suggested that apigenin was able to bind to most of the proteins similar to the inhibitory molecules of its native structure. A remarkable reduction in the total energy after energy minimization of apigenin-antiapoptotic protein complexes suggested increased stability of the docked complexes. The same complexes were found to be stable over a 10 ns period of molecular simulation at 300 K. These findings advocated the study to evaluate apigenin's potential to inhibit the HeLa cells at 5, 10, and 15 μM concentrations in the clonogenic assay. Apigenin inhibited the colony-forming ability of HeLa cells in a dose-dependent manner over a fortnight. Light microscopy of the treated cells displayed the morphological evidence characteristic of apoptosis in HeLa cells such as blebbing, spike formation, cytoplasmic oozing, and nuclear fragmentation. Thus, these results clearly indicate that apigenin may be used as a potential chemopreventive agent in cervical cancer management.

Assessment of fragment docking and scoring with the endothiapepsin model system

Fragment-based screening has become indispensable in drug discovery. Yet, the weak binding affinities of these small molecules still represent a challenge for the reliable detection of fragment hits. The extent of this issue was illustrated in the literature for the aspartic protease endothiapepsin: When seven biochemical and biophysical in vitro screening methods were applied to screen a library of 361 fragments, very poor overlap was observed between the hit fragments identified by the individual approaches, resulting in high levels of false positive and/or false negative results depending on the mutually compared methods. Here, the reported in vitro findings are juxtaposed with the results from in silico docking and scoring approaches. The docking programs GOLD and Glide were considered with the scoring functions ASP, ChemScore, ChemPLP, GoldScore, DSXCSD, and GlideScore. First, the ranking power and scoring power were assessed for the named scoring functions. Second, the capability of reproducing the crystallized fragment binding modes was tested in a structure-based redocking approach. The redocking success notably depended on the ligand efficiency of the considered fragments. Third, a blinded virtual screening approach was employed to evaluate whether in silico screening can compete with in vitro methods in the enrichment of fragment databases.

Unleashing the power of generative AI in drug discovery

Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.

A comprehensive examination of ACE2 receptor and prediction of spike glycoprotein and ACE2 interaction based on in silico analysis of ACE2 receptor

The ACE2 receptor plays a vital role not only in the SARS-CoV-induced epidemic but also in various other diseases, including cardiovascular diseases and ARDS. While studies have explored the interactions between ACE2 and SARS-CoV proteins, comprehensive research utilizing bioinformatic tools on the ACE2 protein has been lacking. The one aim of present study was to extensively analyze the regions of the ACE2 protein. After utilizing all bioinformatics tools especially G104 and L108 regions on ACE2 were come forward. The results of our analysis revealed that possible mutations or deletions in the G104 and L108 regions play a critical role in both the biological functioning and the determination of the chemical-physical properties of ACE2. Additionally, these regions were found to be more susceptible to mutations or deletions compared to other regions of the ACE2 protein. Notably, the randomly selected peptide, LQQNGSSVLS (100-109), which includes G104 and L108, exhibited a crucial role in binding the RBD of the spike protein, as supported by docking scores. Furthermore, both MDs and iMODs results provided evidence that G104 and L108 influence the dynamics of ACE2-spike complexes. This study is expected to offer a new perspective on the ACE2-SARS-CoV interaction and other research areas where ACE2 plays a significant role, such as biotechnology (protein engineering, enzyme optimization), medicine (RAS, pulmonary and cardiac diseases), and basic research (structural motifs, stabilizing protein folds, or facilitating important inter molecular contacts, protein's proper structure and function).Communicated by Ramaswamy H. Sarma.

Biochemical and biophysical interaction of rare earth elements with biomacromolecules: A comprehensive review

The growing utilization of rare earth elements (REEs) in industrial and technological applications has captured global interest, leading to the development of high-performance technologies in medical diagnosis, agriculture, and other electronic industries. This accelerated utilization has also raised human exposure levels, resulting in both favourable and unfavourable impacts. However, the effects of REEs are dependent on their concentration and molecular species. Therefore, scientific interest has increased in investigating the molecular interactions of REEs with biomolecules. In this current review, particular attention was paid to the molecular mechanism of interactions of Lanthanum (La), Cerium (Ce), and Gadolinium (Gd) with biomolecules, and the biological consequences were broadly interpreted. The review involved gathering and evaluating a vast scientific collection which primarily focused on the impact associated with REEs, ranging from earlier reports to recent discoveries, including studies in human and animal models. Thus, understanding the molecular interactions of each element with biomolecules will be highly beneficial in elucidating the consequences of REEs accumulation in the living organisms.

Leveraging shape screening and molecular dynamics simulations to optimize PARP1-Specific chemo/radio-potentiators for antitumor drug design

PARP1 plays a pivotal role in DNA repair within the base excision pathway, making it a promising therapeutic target for cancers involving BRCA mutations. Current study is focused on the discovery of PARP inhibitors with enhanced selectivity for PARP1. Concurrent inhibition of PARP1 with PARP2 and PARP3 affects cellular functions, potentially causing DNA damage accumulation and disrupting immune responses. In step 1, a virtual library of 593 million compounds has been screened using a shape-based screening approach to narrow down the promising scaffolds. In step 2, hierarchical docking approach embedded in Schrödinger suite was employed to select compounds with good dock score, drug-likeness and MMGBSA score. Analysis supplemented with decomposition energy, molecular dynamics (MD) simulations and hydrogen bond frequency analysis, pinpointed that active site residues; H862, G863, R878, M890, Y896 and F897 are crucial for specific binding of ZINC001258189808 and ZINC000092332196 with PARP1 as compared to PARP2 and PARP3. The binding of ZINC000656130962, ZINC000762230673, ZINC001332491123, and ZINC000579446675 also revealed interaction involving two additional active site residues of PARP1, namely N767 and E988. Weaker or no interaction was observed for these residues with PARP2 and PARP3. This approach advances our understanding of PARP-1 specific inhibitors and their mechanisms of action, facilitating the development of targeted therapeutics.

DiffBindFR: an SE(3) equivariant network for flexible protein-ligand docking

Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurate prediction of protein-ligand binding mode is essential. Conventional docking approaches perform well in redocking tasks with known protein binding pocket conformation in the complex state. However, in real-world docking scenario without knowing the protein binding conformation for a new ligand, accurately modeling the binding complex structure remains challenging as flexible docking is computationally expensive and inaccurate. Typical deep learning-based docking methods do not explicitly consider protein side chain conformations and fail to ensure the physical plausibility and detailed atomic interactions. In this study, we present DiffBindFR, a full-atom diffusion-based flexible docking model that operates over the product space of ligand overall movements and flexibility and pocket side chain torsion changes. We show that DiffBindFR has higher accuracy in producing native-like binding structures with physically plausible and detailed interactions than available docking methods. Furthermore, in the Apo and AlphaFold2 modeled structures, DiffBindFR demonstrates superior advantages in accurate ligand binding pose and protein binding conformation prediction, making it suitable for Apo and AlphaFold2 structure-based drug design. DiffBindFR provides a powerful flexible docking tool for modeling accurate protein-ligand binding structures.

Drug repurposing for respiratory infections

Respiratory infections such as Coronavirus disease 2019 are a substantial worldwide health challenge, frequently resulting in severe sickness and death, especially in susceptible groups. Conventional drug development for respiratory infections faces obstacles such as extended timescales, substantial expenses, and the rise of resistance to current treatments. Drug repurposing is a potential method that has evolved to quickly find and reuse existing medications for treating respiratory infections. Drug repurposing utilizes medications previously approved for different purposes, providing a cost-effective and time-efficient method to tackle pressing medical needs. This chapter summarizes current progress and obstacles in repurposing medications for respiratory infections, focusing on notable examples of repurposed pharmaceuticals and their probable modes of action. The text also explores the significance of computational approaches, high-throughput screening, and preclinical investigations in identifying potential candidates for repurposing. The text delves into the significance of regulatory factors, clinical trial structure, and actual data in confirming the effectiveness and safety of repurposed medications for respiratory infections. Drug repurposing is a valuable technique for quickly increasing the range of treatments for respiratory infections, leading to better patient outcomes and decreasing the worldwide disease burden.

Molecular Docking Approach for Biological Interaction of Green Synthesized Nanoparticles

In recent years, significant progress has been made in the subject of nanotechnology, with a range of methods developed to synthesize precise-sized and shaped nanoparticles according to particular requirements. Often, the nanoparticles are created by employing dangerous reducing chemicals to reduce metal ions into uncharged nanoparticles. Green synthesis or biological approaches have been used recently to circumvent this issue because biological techniques are simple, inexpensive, safe, clean, and extremely productive. Nowadays, much research is being conducted on how different kinds of nanoparticles connect to proteins and nucleic acids using molecular docking models. Therefore, this review discusses the most recent advancements in molecular docking capacity to predict the interactions between various nanoparticles (NPs), such as ZnO, CuO, Ag, Au, and Fe3O4, and biological macromolecules.

Bacterial Glycolipid Acting on Protein Transport Across Membranes

The process of protein transport across membranes involves a variety of factors and has been extensively investigated. Traditionally, proteinaceous translocons and chaperones have been recognized as crucial factors in this process. However, recent studies have highlighted the significant roles played by lipids and a glycolipid present in biological membranes in membrane protein transport. Membrane lipids can influence transport efficiency by altering the physicochemical properties of membranes. Notably, our studies have revealed that diacylglycerol (DAG) attenuates mobility in the membrane core region, leading to a dramatic suppression of membrane protein integration. Conversely, a glycolipid in Escherichia coli inner membranes, named membrane protein integrase (MPIase), enhances integration not only through the alteration of membrane properties but also via direct interactions with membrane proteins. This review explores the mechanisms of membrane protein integration mediated by membrane lipids, specifically DAG, and MPIase. Our results, along with the employed physicochemical analysis methods such as fluorescence measurements, nuclear magnetic resonance, surface plasmon resonance, and docking simulation, are presented to elucidate these mechanisms.

Bioinformatics Approaches in the Development of Antifungal Therapeutics and Vaccines

Fungal infections are considered a great threat to human life and are associated with high mortality and morbidity, especially in immunocompromised individuals. Fungal pathogens employ various defense mechanisms to evade the host immune system, which causes severe infections. The available repertoire of drugs for the treatment of fungal infections includes azoles, allylamines, polyenes, echinocandins, and antimetabolites. However, the development of multidrug and pandrug resistance to available antimycotic drugs increases the need to develop better treatment approaches. In this new era of -omics, bioinformatics has expanded options for treating fungal infections. This review emphasizes how bioinformatics complements the emerging strategies, including advancements in drug delivery systems, combination therapies, drug repurposing, epitope-based vaccine design, RNA-based therapeutics, and the role of gut-microbiome interactions to combat anti-fungal resistance. In particular, we focused on computational methods that can be useful to obtain potent hits, and that too in a short period.

Phytoconstituents of Artemisia Annua as potential inhibitors of SARS CoV2 main protease: an in silico study

BACKGROUND

In November 2019, the world faced a pandemic called SARS-CoV-2, which became a major threat to humans and continues to be. To overcome this, many plants were explored to find a cure.

METHODS

Therefore, this research was planned to screen out the active constituents from Artemisia annua that can work against the viral main protease Mpro as this non-structural protein is responsible for the cleavage of replicating enzymes of the virus. Twenty-five biocompounds belonging to different classes namely alpha-pinene, beta-pinene, carvone, myrtenol, quinic acid, caffeic acid, quercetin, rutin, apigenin, chrysoplenetin, arteannunin b, artemisinin, scopoletin, scoparone, artemisinic acid, deoxyartemisnin, artemetin, casticin, sitogluside, beta-sitosterol, dihydroartemisinin, scopolin, artemether, artemotil, artesunate were selected. Virtual screening of these ligands was carried out against drug target Mpro by CB dock.

RESULTS

Quercetin, rutin, casticin, chrysoplenetin, apigenin, artemetin, artesunate, sopolin and sito-gluside were found as hit compounds. Further, ADMET screening was conducted which represented Chrysoplenetin as a lead compound. Azithromycin was used as a standard drug. The interactions were studied by PyMol and visualized in LigPlot. Furthermore, the RMSD graph shows fluctuations at various points at the start of simulation in Top1 (Azithromycin) complex system due to structural changes in the helix-coil-helix and beta-turn-beta changes at specific points resulting in increased RMSD with a time frame of 50 ns. But this change remains stable after the extension of simulation time intervals till 100 ns. On other side, the Top2 complex system remains highly stable throughout the time scale. No such structural dynamics were observed bu the ligand attached to the active site residues binds strongly.

CONCLUSION

This study facilitates researchers to develop and discover more effective and specific therapeutic agents against SARS-CoV-2 and other viral infections. Finally, chrysoplenetin was identified as a more potent drug candidate to act against the viral main protease, which in the future can be helpful.

New oxomethacrylate and acetamide: synthesis, characterization, and their computational approaches: molecular docking, molecular dynamics, and ADME analyses

The compounds 2-chloro-N-(3-methoxyphenyl)acetamide (m-acetamide) and 2-(3-methoxyphenylamino)-2-oxoethyl methacrylate (3MPAEMA) were synthesized in this study for the first time in the literature. FTIR, 1H, and 13C NMR spectroscopic techniques were used to characterize it. Subsequently, computational techniques were used to assess various ADME factors, such as drug-likeness properties, bioavailability score, and adherence to Lipinski's rule. Finally, molecular docking experiments were conducted with the human topoisomerase α2 (TOP2A) protein to verify and validate the reliability and stability of the docking procedure. The results of the docking scores, which quantify binding affinity, indicated that these derivatives exhibited a stronger affinity for TOP2A.

Exploring the potential of drug repurposing for treating depression

Researchers are interested in drug repurposing or drug repositioning of existing pharmaceuticals because of rising costs and slower rates of new medication development. Other investigations that authorized these treatments used data from experimental research and off-label drug use. More research into the causes of depression could lead to more effective pharmaceutical repurposing efforts. In addition to the loss of neurotransmitters like serotonin and adrenaline, inflammation, inadequate blood flow, and neurotoxins are now thought to be plausible mechanisms. Because of these other mechanisms, repurposing drugs has resulted for treatment-resistant depression. This chapter focuses on therapeutic alternatives and their effectiveness in drug repositioning. Atypical antipsychotics, central nervous system stimulants, and neurotransmitter antagonists have investigated for possible repurposing. Nonetheless, extensive research is required to ensure their formulation, effectiveness, and regulatory compliance.

Discovery of Novel Aldose Reductase Inhibitors via the Integration of Ligand-Based and Structure-Based Virtual Screening with Experimental Validation

Aldose reductase plays a central role in diabetes mellitus (DM) associated complications by converting glucose to sorbitol, resulting in a harmful increase of reactive oxygen species (ROS) in various tissues, such as the heart, vasculature, neurons, eyes, and kidneys. We employed a comprehensive approach, integrating both ligand- and structure-based virtual screening followed by experimental validation. Initially, candidate compounds were extracted from extensive drug and chemical libraries using the DeepChem's GraphConvMol algorithm, leveraging its capacity for robust molecular feature representation. Subsequent refinement employed molecular docking and molecular dynamics (MD) simulations, which are crucial for understanding compound-receptor interactions and dynamic behavior in a simulated physiological environment. Finally, the candidate compounds were subjected to experimental validation of their biological activity using an aldose reductase inhibitor screening kit. The comprehensive approach led to the identification of a promising compound, demonstrating significant potential as an aldose reductase inhibitor. This comprehensive approach not only yields a potential therapeutic intervention for DM-related complications but also establishes an integrated protocol for drug development, setting a new benchmark in the field.

An overview of aptamer: Design strategy, prominent applications, and potential challenge in plants

Aptamers, serving as highly efficient molecular recognition and biotechnology tools, have garnered increasing interest in the realm of plant science in recent years. Aptamers are synthetic single-stranded short nucleotides or peptides, that bind targets with high specificity and affinity, triggering precise biological responses. As an alternative to antibodies, aptamers present promising avenues for advancement in biological researches. Aptamers function in a range of fields, encompassing cell signaling, drug development, biosensor technology, as well as botany, agricultural and forestry sciences. In this review, we introduce classifications and screening methods of aptamers, as well as aptamer-based technologies, highlighting their significant contributions to recent advancements. With their powerful functionality and ability to bind targets with high specificity and affinity, aptamers offer promising opportunities for breakthroughs in plant research.

Alternatives of Animal Models for Biomedical Research: a Comprehensive Review of Modern Approaches

Biomedical research has long relied on animal models to unravel the intricacies of human physiology and pathology. However, concerns surrounding ethics, expenses, and inherent species differences have catalyzed the exploration of alternative avenues. The contemporary alternatives to traditional animal models in biomedical research delve into three main categories of alternative approaches: in vitro models, in vertebrate models, and in silico models. This unique approach to artificial intelligence and machine learning has been a keen interest to be used in different biomedical research. The main goal of this review is to serve as a guide to researchers seeking novel avenues for their investigations and underscores the importance of considering alternative models in the pursuit of scientific knowledge and medical breakthroughs, including showcasing the broad spectrum of modern approaches that are revolutionizing biomedical research and leading the way toward a more ethical, efficient, and innovative future. Models can insight into cellular processes, developmental biology, drug interaction, assessing toxicology, and understanding molecular mechanisms.

Physics-Informed Generative Model for Drug-like Molecule Conformers

We present a diffusion-based generative model for conformer generation. Our model is focused on the reproduction of the bonded structure and is constructed from the associated terms traditionally found in classical force fields to ensure a physically relevant representation. Techniques in deep learning are used to infer atom typing and geometric parameters from a training set. Conformer sampling is achieved by taking advantage of recent advancements in diffusion-based generation. By training on large, synthetic data sets of diverse, drug-like molecules optimized with the semiempirical GFN2-xTB method, high accuracy is achieved for bonded parameters, exceeding that of conventional, knowledge-based methods. Results are also compared to experimental structures from the Protein Databank and the Cambridge Structural Database.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Homology modeling and molecular docking studies to decrease glutamine affinity of Yarrowia lipolytica L-asparaginase

L-Asparaginase is a key component in the treatment of leukemias and lymphomas. However, the glutamine affinity of this therapeutic enzyme is an off-target activity that causes several side effects. The modeling and molecular docking study of Yarrowia lipolytica L-asparaginase (YL-ASNase) to reduce its l-glutamine affinity and increase its stability was the aim of this study. Protein-ligand interactions of wild-type and different mutants of YL-ASNase against L-asparagine compared to l-glutamine were assessed using AutoDock Vina tools because the crystal structure of YL-ASNase does not exist in the protein data banks. The results showed that three mutants, T171S, T171S-N60A, and T171A-T223A, caused a considerable increase in L-asparagine affinity and a decrease in l-glutamine affinity as compared to the wild-type and other mutants. Then, molecular dynamics simulation and MM/GBSA free energy were applied to assess the stability of protein structure and its interaction with ligands. The three mutated proteins, especially T171S-N60A, had higher stability and interactions with L-asparagine than l-glutamine in comparison with the wild-type. The YL-ASNase mutants could be introduced as appropriate therapeutic candidates that might cause lower side effects. However, the functional properties of these mutated enzymes need to be confirmed by genetic manipulation and in vitro and in vivo studies.

Electronic tongue, proton-transfer-reaction mass spectrometry, spectral analysis, and molecular docking characterization for determining the effect of α-amylase on flavor perception

The effects of α-amylase on of flavor perception were investigated via spectrum analysis, electronic tongue, on-line mass spectrometry, and molecular docking. Aroma release results showed that α-amylase exhibited variable release patterns of different aroma compounds. Electronic tongue analysis showed that the perception of bitterness, sweetness, sour, and saltiness was subtly increased and that of umami was significantly increased (p < 0.01) along with the increasing enzyme activity of α-amylase. Ultraviolet absorption and fluorescence spectroscopy analyses showed that static quenching occurred between α-amylase and eight flavor compounds and their interaction effects were spontaneous. One binding pocket was confirmed between the α-amylase and flavor compounds, and molecular docking simulation results showed that the hydrogen, electrostatic, and hydrophobic bonds were the main force interactions. The TYP82, TRP83, LEU173, HIS80, HIS122, ASP297, ASP206, and ARG344 were the key α-amylase amino acid residues that interacted with the eight flavor compounds.

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion

Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug repositioning. However, traditional experimental methods have disadvantages such as long operation cycles, high manpower requirements, and high economic costs, making it difficult to predict specific interactions between drugs and target proteins quickly and accurately. Some methods mainly use the SMILES sequence of drugs and the primary structure of proteins as inputs, ignoring the graph information such as bond encoding, degree centrality encoding, spatial encoding of drug molecule graphs, and the structural information of proteins such as secondary structure and accessible surface area. Moreover, previous methods were based on protein sequences to learn feature representations, neglecting the completeness of information. To address the completeness of drug and protein structure information, we propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA). Our method reduces prediction errors caused by insufficient feature learning. Experimental results on Davis and KIBA datasets showed a better prediction of drugtarget affinity than existing affinity prediction methods.

Deep learning in structural bioinformatics: current applications and future perspectives

In this review article, we explore the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by extensive data, accessible toolkits and robust computing resources. As big data continue to advance, DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. Our comprehensive review provides detailed insights into DL, featuring specific demonstrations of its notable applications in bioinformatics. We address challenges tailored for DL, spotlight recent successes in structural bioinformatics and present a clear exposition of DL-from basic shallow neural networks to advanced models such as convolution, recurrent, artificial and transformer neural networks. This paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics.

Identifying Biomarkers and Therapeutic Targets by Multiomic Analysis for HNSCC: Precision Medicine and Healthcare Management

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the major types of cancer, with 900,000 cases and over 400,000 deaths annually. It constitutes 3-4% of all cancers in Europe and western countries. As early diagnosis is the key to treating the disease, reliable biomarkers play an important role in the precision medicine of HNSCC. Despite treatments, the survival rate of cancer patients remains unchanged, and this is mainly due to the failure to detect the disease early. Thus, the objective of this study is to identify reliable biomarkers for head and neck cancers for better healthcare management. Methods: In this study, all available, curated human genes were screened for their expression against HNSCC TCGA patient samples using genomic and proteomic data by various bioinformatic approaches and datamining. Docking studies were performed using AutoDock or online virtual screening tools for identifying potential ligands. Results: Sixty genes were short-listed, and most of them show a consistently higher expression in head and neck patient samples at both the mRNA and the protein level. Irrespective of human papillomavirus (HPV) status, all of them show a higher expression in cancer samples. The higher expression of 30 genes shows adverse effects on patient survival. Out of the 60 genes, 12 genes have crystal structures and druggable potential. We show that genes such as GTF2H4, HAUS7, MSN, and MNDA could be targets of Pembrolizumab and Nivolumab, which are approved monoclonal antibodies for HNSCC. Conclusion: Sixty genes are identified as potential biomarkers for head and neck cancers based on their consistent and statistically significantly higher expression in patient samples. Four proteins have been identified as potential drug targets based on their crystal structure. However, the utility of these candidate genes has to be further tested using patient samples.

Tire additives: Evaluation of joint toxicity, design of new derivatives and mechanism analysis of free radical oxidation

N-(1,3-Dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD) is one of the most widely used antioxidant agents in tire additives. Its ozonation by-product 6PPD-quinone has recently been recognized as inducing acute mortality in aquatic organisms such as coho salmon. In this study, we aimed to develop an in-silico method to design environmentally friendly 6PPD derivatives and evaluate the joint toxicity of 6PPD with other commonly used tire additives on coho salmon through full factorial design-molecular docking and molecular dynamic simulation. The toxicity mentioned in this study is represented by the binding energy of chemical(s) binding to the coho salmon growth hormone. The recommended formula for tire additives with relatively low toxicity was then proposed. To further reduce the toxicity of 6PPD, 129 6PPD derivatives were designed based on the N-H bond dissociation reaction, and three of these derivatives showed improved antioxidant activity and 6PPD-106 was finally screened as the optimum alternative with lower toxicity to coho salmon. Besides, the mechanism of free radical oxidation (i.e., antioxidation and ozonation metabolic pathway) for 6PPD-106 was also analyzed and found that after ozonation, the toxicity of 6PPD-106's by-products is much lower than that of 6PPD's by-products. This study provided a molecular modelling-based examination of 6PPD, which comprehensively advanced the understanding of 6PPD's environmental behaviors and provided more environmentally friendly 6PPD alternatives with desired functional property and lower ecological risks.