Comprehensive 3D-QSAR and binding mode of BACE-1 inhibitors using R-group search and molecular docking

Network Models of BACE-1 Inhibitors: Exploring Structural and Biochemical Relationships

This study investigates the clustering patterns of human β-secretase 1 (BACE-1) inhibitors using complex network methodologies based on various distance functions, including Euclidean, Tanimoto, Hamming, and Levenshtein distances. Molecular descriptor vectors such as molecular mass, Merck Molecular Force Field (MMFF) energy, Crippen partition coefficient (ClogP), Crippen molar refractivity (MR), eccentricity, Kappa indices, Synthetic Accessibility Score, Topological Polar Surface Area (TPSA), and 2D/3D autocorrelation entropies are employed to capture the diverse properties of these inhibitors. The Euclidean distance network demonstrates the most reliable clustering results, with strong agreement metrics and minimal information loss, indicating its robustness in capturing essential structural and physicochemical properties. Tanimoto and Hamming distance networks yield valuable clustering outcomes, albeit with moderate performance, while the Levenshtein distance network shows significant discrepancies. The analysis of eigenvector centrality across different networks identifies key inhibitors acting as hubs, which are likely critical in biochemical pathways. Community detection results highlight distinct clustering patterns, with well-defined communities providing insights into the functional and structural groupings of BACE-1 inhibitors. The study also conducts non-parametric tests, revealing significant differences in molecular descriptors, validating the clustering methodology. Despite its limitations, including reliance on specific descriptors and computational complexity, this study offers a comprehensive framework for understanding molecular interactions and guiding therapeutic interventions. Future research could integrate additional descriptors, advanced machine learning techniques, and dynamic network analysis to enhance clustering accuracy and applicability.

Computer-Aided and AILDE Approaches to Design Novel 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is a pivotal enzyme in tocopherol and plastoquinone synthesis and a potential target for novel herbicides. Thirty-five pyridine derivatives were selected to establish a Topomer comparative molecular field analysis (Topomer CoMFA) model to obtain correlation information between HPPD inhibitory activity and the molecular structure. A credible and predictive Topomer CoMFA model was established by "split in two R-groups" cutting methods and fragment combinations (q² = 0.703, r² = 0.957, ONC = 6). The established model was used to screen out more active compounds and was optimized through the auto in silico ligand directing evolution (AILDE) platform to obtain potential HPPD inhibitors. Twenty-two new compounds with theoretically good HPPD inhibition were obtained by combining the high-activity contribution substituents in the existing molecules with the R-group search via Topomer search. Molecular docking results revealed that most of the 22 fresh compounds could form stable π-π interactions. The absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction and drug-like properties made 9 compounds potential HPPD inhibitors. Molecular dynamics simulation indicated that Compounds Y12 and Y14 showed good root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values and stability. According to the AILDE online verification, 5 new compounds with potential HPPD inhibition were discovered as HPPD inhibitor candidates. This study provides beneficial insights for subsequent HPPD inhibitor design.

Topomer CoMFA and HQSAR Study on Benzimidazole Derivative as NS5B Polymerase Inhibitor

Background:

In recent years, the number of people infected with the hepatitis C virus (HCV) is increasing rapidly. This has become a major threat to global health, therefore, new anti- HCV drugs are urgently needed. HCV NS5B polymerase is an RNA-dependent RNA polymerase (RdRp), which plays an important role in virus replication, and can effectively prevent the replication of HCV sub-genomic RNA in daughter cells. It is considered a very promising HCV therapeutic target for the design of anti-HCV drugs.

Methods:

In order to explore the relationship between the structure of benzimidazole derivative and its inhibitory activity on NS5B polymerase, holographic quantitative structure-activity relationship (HQSAR) and Topomer comparative molecular field analysis (CoMFA) were used to establish benzimidazole QSAR model of derivative inhibitors.

Results:

The results show that for the Topomer CoMFA model, the cross-validation coefficient q2 value is 0.883, and the non-cross-validation coefficient r2 value is 0.975. The model is reasonable, reliable, and has a good predictive ability. For the HQSAR model, the cross-validated q2 value is 0.922, and the uncross-validated r2 value is 0.971, indicating that the model data fit well and has a high predictive ability. Through the analysis of the contour map and color code diagram, 40 new benzimidazole inhibitor molecules were designed, and all of them have higher activity than template molecules, and the new molecules have significant interaction sites with protein 3SKE.

Conclusion:

The 3D-QSAR model established by Topomer CoMFA and HQSAR has good prediction results and the statistical verification is valid. The newly designed molecules and docking results provide theoretical guidance for the synthesis of new NS5B polymerase inhibitors and for the identification of key residues that the inhibitors bind to NS5B, which helps to better understand their inhibitory mechanism. These findings are helpful for the development of new anti-HCV drugs.

Molecular design, molecular docking and ADMET study of cyclic sulfonamide derivatives as SARS-CoV-2 inhibitors

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) continues to spread globally with more than 172 million confirmed cases and 3.57 million deaths. Cyclic sulfonamide derivative is identified as a successful compound and showed anti-SARS-CoV-2 activity. In this study, the structure and activity relationships of 35 cyclic sulfonamide compound inhibitors are investigated by using three-dimensional quantitative structure-activity relationship (3D-QSAR) and holographic quantitative structure-activity relationship (HQSAR). Two models with good statistical parameters and reliable predictive ability are obtained from the same training set, including Topomer CoMFA (q2= 0.623,r2= 0.938,rpred2= 0.893) model and HQSAR (q2= 0.704,r2= 0.958,rpred2=0.779) model. The established models not only have good stability, but also show good external prediction ability for the test set. The contour and color code maps of the models provide a lot of useful information for determining the structural requirements which might affect the activity; this information paves the way for the design of four novel cyclic sulfonamide compounds, and predictes their pIC₅₀ values. We explore the interaction between the newly designed molecule and SARS-CoV-2 3CLpro by molecular docking. The docking results show that GLU166, GLN192, ALA194, and VAL186 may be the potential active residues of the SARS-CoV-2 inhibitor evaluated in this study. Finally, the oral bioavailability and toxicity of the newly designed cyclic sulfonamide compounds are evaluated and the results show that the four newly designed cyclic sulfonamide compounds have major ADMET properties and can be used as reliable inhibitors against COVID-19. These results may provide useful insights for the design of effective SARS-CoV-2 inhibitors.

Network-based identification of miRNAs and transcription factors and in silico drug screening targeting δ-secretase involved in Alzheimer's disease

BACKGROUND

System medicine approaches have played a pivotal role in identifying novel disease networks especially in miRNA research. It is no wonder that miRNAs are implicated in multiple clinical conditions, allowing us to establish the hubs and nodes for network models of Alzheimer's Disease (AD). AD is an age-related, progressive, irreversible, and multifactorial neurodegenerative disorder characterized by cognitive and memory impairment and is the most common cause of dementia in older adults. Worldwide, around 50 million people have dementia, and there are nearly 10 million new cases every year. δ-secretase, also known as asparagine endopeptidase (AEP) or legumain (LGMN), is a lysosomal cysteine protease that cleaves peptide bonds C-terminally to asparagine residues in both amyloid precursor protein (APP) and tau, mediating the amyloid-β and tau pathology in AD. The patient's miRNA expression was found to be deregulated in the brain, extracellular fluid, blood plasma, and serum.

METHODS

Protein-Protein Interaction (PPI) networks of LGMN or δ-secretase were constructed using the Genemania database. Network Analyzer, a Cytoscape plugin, analyzed the network topological properties of LGMN. miRNAs related to Alzheimer's were extracted from the HMDD (Human microRNA Disease Database) and experimentally verified miRNA-gene interaction was obtained by searching miRWalk. Starbase v2.0 and miRanda were used for screening miRNA of LGMN genes. Moreover, to understand the regulatory mechanism in AD, we have screened major transcription factors of LGMN targeted genes using the Network Analyst 3.0, TRRUST (v2.0) server, and ENCODE. The Genotype-Tissue Expression (GTEx) and BEST tool were used to investigate the expression pattern of the LGMN gene. In parallel, we performed in-silico drug designing of the novel inhibitor scaffold of δ-secretase as powerful therapeutic targets by using the concept of scaffolds and frameworks. In this context, this study also aimed at identifying effective small molecule inhibitors targeting δ-secretase.

RESULTS

Among the 16 experimentally verified miRNAs, Network analysis of the LGMN and its associated miRNA identify novel hsa-miRNA-106a-5p and hsa-miRNA-34a-5p being more expressed in the brain. Our in silico high throughput screening, followed by XP docking revealed Oprea1 as the lead. Molecular dynamic simulations of the δ-secretase-docked complex have been carried out for a time period of 200 ns and revealed that Root Mean Square Deviation (RMSD) of the protein Cα-backbone with respect to its starting position increased to 1.20 Å for the first 25 ns of the trajectory and then became stable around 0.6 Å in the last 170 ns course of the simulation. The radius of gyration (RGYR) reveals that compactness was maintained till the end of simulations.

CONCLUSION

Network analysis of LGMN associated miRNAs lead to the identification of two novel miRNAs, being highly expressed in the brain. This study also lead to the identification and expression of 10 Transcription factors associated with LGMN. Expression Heatmap results show high and continuous expression of LGMN in most of the regions of the brain, especially in the frontal cortex. Further, in silico drug analysis led us to the identification of Oprea1 which could be taken for further investigation to explore its potential for AD therapy.

Classification of beta-site amyloid precursor protein cleaving enzyme 1 inhibitors by using machine learning methods

The beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a transmembrane aspartyl-protease, that cleaves amyloid precursor protein (APP) at the β-site. The sequential proteolytic cleavage of APP, first by β-secretase and then by γ-secretase complex, leads to the production and release of amyloid-β peptide, a pathological hallmark of Alzheimer's disease (AD). BACE1 inhibitors are reported to possess considerable potential in decreasing the level of amyloid-β in brain and preventing the progression of AD. A classification study has been conducted on 3536 diverse BACE1 inhibitors, obtained from Binding DB database, by extracting two types of descriptors, that is molecular property (Mordred) and fingerprints (Pubchem, MACCS and KRFP). Furthermore, based on the descriptors, various machine learning algorithms such as Naïve Bayesian (NB), nearest known neighbours (kNN), support vector machine (SVM), random forest (RF) and gradient-boosted algorithms (XGB) were applied to develop classification models. The performance of models was evaluated by using accuracy, precision, recall and F1 score of test set. The best NB, kNN, SVM, RF and XGB classifiers had F1 score of 0.74, 0.85, 0.86, 0.87 and 0.87, respectively. The diverse 3536 BACE1 inhibitors were clustered into 11 subsets, and the structural features of each subset were evaluated. The important fragments present in active and inactive compounds were also identified. The model developed in the study would serve as a valuable tool for the designing of BACE1 inhibitors, and also in virtual screening of molecules to identify these.

Design of dipicolinic acid derivatives as New Delhi metallo-β-lactamase-1 inhibitors using a combined computational approach

New Delhi metallo-β-lactamase (NDM-1) is the most recent addition to the class of metallo-β-lactamases (MBLs). This enzyme leads to antibiotic resistance in clinical treatments owing to its exertion of hydrolysis activity in almost all clinically available β-lactam antibiotics. Consequently, inhibitors targeting NDM-1 have attracted considerable research attention. However, progress has been slow regarding the study of the quantitative structure-activity relationship (QSAR) of NDM-1 inhibitors. In this study, a three-dimensional QSAR (3 D-QSAR) for NDM-1 inhibitors was established using Topomer CoMFA. The multiple correlation coefficients of the fitting model, leave-one-out cross validation, and external validation were found to be 0.761, 0.976, and 0.972, respectively. Topomer Search was used to design 16 new molecules that inhibit NDM-1 using R-group search from ZINC databases, 10 of which had comparatively high activities against NDM-1. The results indicate that Topomer CoMFA and Topomer Search can be used to design new NDM-1 inhibitors and guide the design of new NDM-1 drugs with good predictive capability. Furthermore, from molecular modeling and binding free-energy calculation, it was found that the newly designed molecules can bind to the catalytic region of NDM-1. Additionally, the newly designed inhibitors formed strong interactions with Ile35, Met67, Phe70, Trp93, His122, His189, Cys208, and His250 around the Zn²⁺-centered active region of NDM-1. These findings will facilitate the development of more effective NDM-1 inhibitors for use as potential antibacterial agents.Communicated by Ramaswamy H. Sarma.

QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer's Disease

Alzheimer’s disease is one of the most common neurodegenerative disorders in elder population. The β-site amyloid cleavage enzyme 1 (BACE1) is the major constituent of amyloid plaques and plays a central role in this brain pathogenesis, thus it constitutes an auspicious pharmacological target for its treatment. In this paper, a QSAR model for identification of potential inhibitors of BACE1 protein is designed by using classification methods. For building this model, a database with 215 molecules collected from different sources has been assembled. This dataset contains diverse compounds with different scaffolds and physical-chemical properties, covering a wide chemical space in the drug-like range. The most distinctive aspect of the applied QSAR strategy is the combination of hybridization with backward elimination of models, which contributes to improve the quality of the final QSAR model. Another relevant step is the visual analysis of the molecular descriptors that allows guaranteeing the absence of information redundancy in the model. The QSAR model performances have been assessed by traditional metrics, and the final proposed model has low cardinality, and reaches a high percentage of chemical compounds correctly classified.

Two- and three-dimensional QSAR studies on hURAT1 inhibitors with flexible linkers: topomer CoMFA and HQSAR

hURAT1 (human urate transporter 1) is a successful target for hyperuricemia. Recently, the modification work on hURAT1 inhibitors showed that the flexible linkers would benefit biological activity. The study aimed to investigate the contribution of the linkers and give modification strategies on this kind of structures based on QSAR models (HQSAR and topomer CoMFA). The most effective HQSAR and topomer CoMFA models were generated by applying the training set containing 63 compounds, with the cross-validated q² values of 0.869/0.818 and the non-cross-validated correlation coefficients r² of 0.951/0.978, respectively. The Y-randomization test was applied to ensure the robustness of the models. The external predictive correlation coefficient (r_pred²) grounded on the external test set (21 compounds) of two models was 0.910 and 0.907, respectively. In addition, the models were validated by Golbraikh-Tropsha and Roy methods, as well as other statistical metrics. The results showed that both models were reliable. Topomer CoMFA steric/electrostatic contours and HQSAR atomic contribution maps illustrated the structural features which governed their inhibitory potency. The dependable results could provide important insights to guide the designing of more potential hURAT1 inhibitors.

Application of molecular framework-based data-mining method in the search for beta-secretase 1 inhibitors through drug repurposing

Targeting beta-secretase 1, also known as beta-amyloid precursor protein-cleaving enzyme (BACE-1) for the inhibition of amyloid production, has been intensely studied in the last decades in the search for stopping Alzheimer's disease (AD) progression. The chances of finding a druggable BACE-1 inhibitor may be increased by drug repurposing, as this kind of molecules already fulfil certain requirements needed for further advancement. The study describes the development and application of a data-mining method based on molecular frameworks and descriptor values of tested BACE-1 inhibitors, suitable for filtering large compound databases, in order to find molecules with high potency against this protease. A total of 465 compounds extracted from the literature, tested against BACE-1, were analysed for finding molecular descriptor values and frameworks that ensure a high probability of strong inhibition. Resulting conclusions were used for filtering DrugBank database, containing ∼8700 approved and experimental drugs, obtaining 26 structures characterized by four major Bemis-Murcko frameworks: 2-[3-(2-cyclohexylethyl)cyclohexyl]-decahydronaphthalene, 3-(2-cyclohexylethyl)-1,1'-bi(cyclohexane), [5-(cyclohexylmethyl)-8-cyclopentyloctyl]cyclohexane and (3-cyclohexylcyclopentyl)cyclohexane. The compounds were further studied by molecular docking using the structure of the closed form of the enzyme, which revealed seven compounds already involved in trials targeting BACE-1 inhibition, confirming the method's specificity. The compounds that afforded the best binding energies were DB06925 (tyrosine-protein kinase inhibitor), DB12285 (Verubecestat) and DB08899 (Enzalutamide). Moreover, docking results indicated several other molecules with high in silico inhibitory potency that can be further studied for developing a potential treatment for AD. Communicated by Ramaswamy H. Sarma.

In Silico Studies in Drug Research Against Neurodegenerative Diseases

Background

Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computer-aided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays.

Methods

In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches.

Results

Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach.

Conclusion

Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.

Combining in vitro and in silico Approaches to Find New Candidate Drugs Targeting the Pathological Proteins Related to the Alzheimer's Disease

Background: Alzheimer’s disease (AD) as the most common cause of dementia among older people has aroused the universal concern of the whole world. However, until now there is still none effective treatments. Consequently, the development of new drugs targeting this complicated brain disorder is urgent and needs more efforts. In this review, we detailed the current state of knowledge about new candidate drugs targeting the pathological proteins especially the drugs which are employed using the combined methods of in vitro and in silico.

Methods: We looked up and reviewed online papers related to the pathogenesis and new drugs development of AD. Then, articles up to the requirements were respectively analyzed and summaried to provide the latest knowledge about the pathogenic effect and the new candidate drugs targeting Aβ and Tau proteins.

Results: New candidate drugs targeting the Aβ include decreasing the production, promoting the clearence and preventing aggregation. However these drugs have mostly failed in Phase III clinical trial stage due to the unsuccessful of reversing cognition symptoms. As to tau protein, the prevention of tau aggregation and propagation is a promising strategy to synthesize/design mechanism-based drugs against tauopathies. Some candidate drugs are under research. Moreover, because of the complex pathogenesis of AD, multi-target drugs have also shed light on the treatment of AD.

Conclusion: Given to the consecutive failure of Aβ-directed drugs and the feasibilities of tau-targeted therapy, more and more researchers suggested that the AD treatment should be moved from Aβ to tau or focused on considering the soluble form of Aβ and tau as a whole. Moreover, the novel in silico methods also have great potential in drug discovery, drug repositioning, virtual screening of chemical libraries. No matter how many difficulties and challenges in prevention and treatment of AD, we firmly believe that the effective and safe drugs will be found using the combined methods in the immediate future with the global effort.

Changing Paradigm from one Target one Ligand Towards Multi-target Directed Ligand Design for Key Drug Targets of Alzheimer Disease: An Important Role of In Silico Methods in Multi-target Directed Ligands Design

Alzheimer disease (AD) is now considered as a multifactorial neurodegenerative disorder and rapidly increasing to an alarming situation and causing higher death rate. One target one ligand hypothesis does not provide complete solution of AD due to multifactorial nature of the disease and one target one drug fails to provide better treatment against AD. Moreo-ver, currently available treatments are limited and most of the upcoming treatments under clinical trials are based on modulat-ing single target. So, the current AD drug discovery research is shifting towards a new approach for a better solution that simultaneously modulates more than one targets in the neurodegenerative cascade. This can be achieved by network pharma-cology, multi-modal therapies, multifaceted, and/or the more recently proposed term “multi-targeted designed drugs”. Drug discovery project is a tedious, costly and long-term project. Moreover, multi-target AD drug discovery added extra challeng-es such as the good binding affinity of ligands for multiple targets, optimal ADME/T properties, no/less off-target side effect and crossing of the blood-brain barrier. These hurdles may be addressed by insilico methods for an efficient solution in less time and cost as computational methods successfully applied to single target drug discovery project. Here, we are summariz-ing some of the most prominent and computationally explored single targets against AD and further, we discussed a success-ful example of dual or multiple inhibitors for same targets. Moreover, we focused on ligand and structure-based computa-tional approach to design MTDL against AD. However, it is not an easy task to balance dual activity in a single molecule but computational approach such as virtual screening docking, QSAR, simulation and free energy is useful in future MTDLs drug discovery alone or in combination with a fragment-based method. However, rational and logical implementations of computational drug designing methods are capable of assisting AD drug discovery and play an important role in optimizing multi-target drug discovery.

A PM7 dynamic residue-ligand interactions energy landscape of the BACE1 inhibitory pathway by hydroxyethylamine compounds. Part I: The flap closure process

BACE1 is an enzyme of scientific interest because it participates in the progression of Alzheimer's disease. Hydroxyethylamines (HEAs) are a family of compounds which exhibit inhibitory activity toward BACE1 at a nanomolar level, favorable pharmacokinetic properties and oral bioavailability. The first step in the inhibition of BACE1 by HEAs consists of their entrance into the protease active site and the resultant conformational change in the protein, from Apo to closed form. These two conformations differ in the position of an antiparallel loop (called the flap) which covers the entrance to the catalytic site. For BACE1, closure of this flap is vital to its catalytic activity and to inhibition of the enzyme due to the new interactions thereby formed with the ligand. In the present study a dynamic energy landscape of residue-ligand interaction energies (ReLIE) measured for 112 amino acids in the BACE1 active site and its immediate vicinity during the closure of the flap induced by 8 HEAs of different inhibitory power is presented. A total of 6.272 million ReLIE calculations, based on the PM7 semiempirical method, provided a deep and quantitative view of the first step in the inhibition of the aspartyl protease. The information suggests that residues Asp93, Asp289, Thr292, Thr293, Asn294 and Arg296 are anchor points for the ligand, accounting for approximately 45% of the total protein-ligand interaction. Additionally, flap closure improved the BACE1-HEA interaction by around 25%. Furthermore, the inhibitory activity of HEAs could be related to the capacity of these ligands to form said anchor point interactions and maintain them over time: the lack of some of these anchor interactions delayed flap closure or impeded it completely, or even caused the flap to reopen. The methodology employed here could be used as a tool to evaluate future structural modifications which lead to improvements in the favorability and stability of BACE1-HEA ReLIEs, aiding in the design of better inhibitors.

Combined HQSAR, topomer CoMFA, homology modeling and docking studies on triazole derivatives as SGLT2 inhibitors

Aim: Sodium–glucose cotransporter 2 (SGLT2) is a promising target for diabetes therapy. We aimed to develop computational approaches to identify structural features for more potential SGLT2 inhibitors. Materials & methods: In this work, 46 triazole derivatives as SGLT2 inhibitors were studied using a combination of several approaches, including hologram quantitative structure–activity relationships (HQSAR), topomer comparative molecular field analysis (CoMFA), homology modeling, and molecular docking. HQSAR and topomer CoMFA were used to construct models. Molecular docking was conducted to investigate the interaction of triazole derivatives and homology modeling of SGLT2, as well as to validate the results of the HQSAR and topomer CoMFA models. Results: The most effective HQSAR and topomer CoMFA models exhibited noncross-validated correlation coefficients of 0.928 and 0.891 for the training set, respectively. External predictions were made successfully on a test set and then compared with previously reported models. The graphical results of HQSAR and topomer CoMFA were proven to be consistent with the binding mode of the inhibitors and SGLT2 from molecular docking. Conclusion: The models and docking provided important insights into the design of potent inhibitors for SGLT2.

Tailored-pharmacophore model to enhance virtual screening and drug discovery: a case study on the identification of potential inhibitors against drug-resistant Mycobacterium tuberculosis (3R)-hydroxyacyl-ACP dehydratases

Aim: Virtual screening (VS) is powerful tool in discovering molecular inhibitors that are most likely to bind to drug targets of interest. Herein, we introduce a novel VS approach, so-called ‘tailored-pharmacophore’, in order to explore inhibitors that overcome drug resistance. Methodology & results: The emergence and spread of drug resistance strains of tuberculosis is one of the most critical issues in healthcare. A tailored-pharmacophore approach was found promising to identify in silico predicted hit with better binding affinities in case of the resistance mutations in MtbHadAB as compared with thiacetazone, a prodrug used in the clinical treatment of tuberculosis. Conclusion: This approach can potentially be enforced for the discovery and design of drugs against a wide range of resistance targets.

Interaction of phenolic acids with trypsin: Experimental and molecular modeling studies

Trypsin is a kind of protease for digestion and food processing, whose activity can be inhibited by phenolic acids in plant foods. However, most reports explained the inhibitory difference of phenolic acids based on the number and position of substituent groups, which failed to reveal the comprehensive inhibitory mechanism. In this work, the inhibitory effects of 11 common phenolic acids on trypsin were investigated. Amongst the tested cinnamic and benzoic acid derivatives, caffeic acid and gallic acid showed the strongest anti-trypsin activity with a noncompetitive inhibition pattern, respectively. The fluorescence analysis displayed that both the quenching rate constant (K_q) and binding constant (K_A) of caffeic acid were higher than those of gallic acid. Molecular docking illustrated their different binding modes with trypsin. The ONIOM calculations revealed that the binding capacity of caffeic acid was higher than that of gallic acid, which could explain their difference in their inhibitory behaviors.

Interaction mechanism exploration of HEA derivatives as BACE1 inhibitors by in silico analysis

The β-site amyloid precursor protein cleaving enzyme 1 (BACE1) initiates the generation of β-amyloid (Aβ) peptides which play a critical early role in the pathogenesis of Alzheimer's disease (AD), and thus it is a prime target for lowering the Aβ levels to treat AD.

Study on the active mechanism of β-secretase inhibitors by molecular simulations

The proteolytic enzyme β-secretase (BACE-1) is one of potential drug targets for treating Alzheimers's disease. First, the reliable and accurate models of three-dimensional quantitative structure-activity relationship for the BACE-1 inhibitors were established, and the several important structural factors that mainly influence the inhibitory activity were obtained. Second, the results of molecular docking presented the binding mode between BACE-1 and its inhibitors, and molecular dynamic simulations provided the details of the receptor-ligand interactions. Furthermore, several new derivatives were designed and validated based on these theoretical analyses. Our studies revealed the binding mechanism between BACE-1 and its inhibitors, and provide some insights into the further structural modification and the design of new inhibitors with higher activity.