Advances and Applications in Bioinformatics and Chemistry最新文献

Purpose: The incidence of cancer, which is a serious public health concern, is increasing. A predictive analysis driven by machine learning was integrated with haematology parameters to create a method for the simultaneous diagnosis of several malignancies at different stages.

Patients and methods: We analysed a newly collected dataset from various hospitals in Jordan comprising 19,537 laboratory reports (6,280 cancer and 13,257 noncancer cases). To clean and obtain the data ready for modelling, preprocessing steps such as feature standardization and missing value removal were used. Several cutting-edge classifiers were employed for the prediction analysis. In addition, we experimented with the dataset's missing values using the histogram gradient boosting (HGB) model.

Results: The feature ranking method demonstrated the ability to distinguish cancer patients from healthy individuals based on hematological features such as WBCs, red blood cell (RBC) counts, and platelet (PLT) counts, in addition to age and creatinine level. The random forest (RF) classifier, followed by linear discriminant analysis (LDA) and support vector machine (SVM), achieved the highest prediction accuracy (ranging from 0.69 to 0.72 depending on the scenario and method investigated), reliably distinguishing between malignant and benign conditions. The HGB model showed improved performance on the dataset.

Conclusion: After investigating a number of machine learning methods, an efficient screening platform for non-invasive cancer detection is provided by the integration of haematological indicators with proper analytical data. Exploring deep learning methods in the future work, could provide insights into more complex patterns within the dataset, potentially improving the accuracy and robustness of the predictions.

Antimicrobial resistance (AMR) represents today a major challenge for global public health, compromising the effectiveness of treatments against a multitude of bacterial infections. In recent decades, artificial intelligence (AI) has emerged as a promising technology for the identification and development of new antibacterial agents. This review focuses on AI methodologies applied to discover new antibacterial candidates. Case studies that identified small molecules and peptides showing antimicrobial activity and demonstrating efficiency against pathogenic resistant bacteria by employing AI are summarized. We also discuss the challenges and opportunities offered by AI, highlighting the importance of AI progress for the identification of new promising antibacterial drug candidates to combat the AMR.

Background: Prostate cancer (PCa) development largely depends on increased levels of oxidative stress (OS) and a deficient anti-oxidative system. Identifying genes associated with oxidative stress is critical in order to direct PCa therapy and future research.

Methods: The TCGA and GTEx databases provided the bulk RNA-seq data, and the GEO database provided the single-cell data GSE141445. Utilizing reactive oxygen species (ROS) markers, single-cell analysis and cluster identification related to oxidative stress were conducted using the R packages "Seurat" and "AUCell". The differentially expressed genes (DEGs) in normal and PCa samples were identified with the "limma" R package. LASSO regression analysis was used to build a recurrence score (RS) model. The R packages "maftools" and the CIBERSORT method were employed to explore genetic mutation and the infiltrating immune cell, respectively. LAMP5 was chosen for further investigation after random forest analysis was performed.

Results: The RS model for PCa was found to be an independent predictor. The tumor immune microenvironment and the frequency of gene mutations differed significantly between the high- and low-risk score groups. Further investigation revealed that downregulation of LAMP5 in PC-3 and DU145 cell lines suppressed cell proliferation and invasion, as demonstrated by the results of in vitro experiments.

Conclusion: We successfully created a robust RS model. The result of the study indicates that LAMP5 could contribute to cell proliferation and invasion in PCa.

Purpose: This study aimed to evaluate the potency of 471 flavonoids from the genus Erythrina as potential acetylcholinesterase (AChE) inhibitors and free radical scavengers through computational studies to develop Alzheimer's disease (AD) therapies from natural products.

Methods: A total of 471 flavonoids from the genus Erythrina were subjected to molecular docking against AChE, followed by toxicity screening. The potential AChE inhibitors with the least toxic profile were subjected to further investigation through molecular dynamics (MD) simulations, density functional theory (DFT) study, and in silico pharmacokinetic predictions.

Results: A combination of molecular docking and in silico toxicity screening led to the identification of 2(S)‒5,7‒dihydroxy‒5'‒methoxy‒[2'',2''‒(3''‒hydroxy)‒dimethylpyrano]‒(5'',6'':3',4') flavanone (89) and Abyssinoflavanone IV (83) as potential AChE inhibitors. These compounds had stable binding to AchE and exhibited lower Root Mean Square Deviation (RMSD) values compared to the apo state of AChE. In addition, Molecular Mechanics Generalized Born Surface Area (MMGBSA) analysis revealed that the binding energies of 89 and 83 were significantly lower compared to acetylcholine, the natural substrate of AChE. Based on DFT study, these compounds exhibited a higher energy in the highest occupied molecular orbital (E_HOMO) and lower electron affinity (EA) than Quercetin. This indicated that 89 and 83 could be potential radical scavengers through their electron-donating activity.

Conclusion: Although this study primarily relied on computational methods, the results showed the dual functionality of compounds 89 and 83 as both potential AChE inhibitors and free radical scavengers. Further investigation in wet laboratory experiments is required to validate their therapeutic potential for AD.

Background: Existing antimalarial drugs primarily target blood-stage parasites, but there is a need for transmission-blocking drugs to combat malaria effectively. Plasmodium falciparum Calcium-dependent Protein Kinase 4 (CDPK4) is a promising target for such drugs. This study employed advanced in silico analyses of hexahydroquinolines (HHQ) derivatives to identify PfCDPK4 inhibitors capable of disrupting malaria transmission. Structure-based virtual screening (SBVS) was employed to discover HHQ derivatives with the highest binding affinities against the 3D structure of PfCDPK4 (PDB 1D: 4QOX).

Methods: Interaction analysis of protein-ligand complexes utilized Discovery Studio Client, while druglikeness and ADMET properties were assessed using SwissADME and pkCSM web servers, respectively. Quantum mechanical calculations of the top hits were conducted using density functional theory (DFT), and GROMACS was employed to perform the molecular dynamics (MD) simulations. Binding free energy was predicted using the MMPBSA.py tool from the AMBER package.

Results: SBVS identified ten best hits possessing docking scores within the range of -11.2 kcal/mol and -10.6 kcal/mol, surpassing the known inhibitor, BKI-1294 (-9.9 kcal/mol). Among these, 4-[4-(Furan-2-carbonyl)piperazin-1-yl]-1-(naphthalen-2-ylmethyl)-2-oxo-4a,5,6,7,8,8a-hexahydroquinoline-3-carbonitrile (PubChem ID: 145784778) exhibited the highest binding affinity (-11.2 kcal/mol) against PfCDPK4.

Conclusion: Comparative analysis of this compound with BKI-1294 using advanced computational approaches demonstrated competitive potential. These findings suggest the potential of 4-[4-(Furan-2-carbonyl)piperazin-1-yl]-1-(naphthalen-2-ylmethyl)-2-oxo-4a,5,6,7,8,8a-hexahydroquinoline-3-carbonitrile as a promising PfCDPK4 inhibitor for disrupting malaria transmission. However, further experimental studies are warranted to validate its efficacy and safety profile.

Background: The Surinam cherry, Eugenia uniflora belongs to the family Myrtaceae, an edible fruit-bearing medicinal plant with various biological properties. Several anticancer studies have been conducted on its essential oils while the non-essential oil compounds including phenolics, flavonoids, and carotenoids have not been fully investigated.

Purpose: Therefore, the study evaluated the in silico anticancer potentials of phenolic, flavonoid, and carotenoid compounds of E. uniflora against the MDM2 and Bcl-xL proteins, which are known to promote cancer cell growth and malignancy. The physicochemical parameters, validation, cytotoxicity, and mutagenicity of the polyphenols were determined using the SwissADME, pkCSM, ProTox-II, and vNN-ADMET online servers respectively. Lastly, the promising phytocompounds were validated using molecular dynamics (MD) simulation.

Results: An extensive literature search resulted in the compilation of forty-four (44) polyphenols from E. uniflora. Top-rank among the screened polyphenols is galloylastragalin, which exhibited a binding energy score of -8.7 and -8.5 kcal/mol with the hydrophobic interactions (Ala93, Val141) and (Leu54, Val93, Ile99), as well as hydrogen bond interactions (Tyr195) and (Gln72) of the proteins Bcl-xL and MDM2 respectively. A complete in silico toxicity assessment revealed that the compounds, galloylastragalin, followed by myricetin, resveratrol, p-Coumaroylquinic acid, and cyanidin-3-O-glucoside, were potentially non-mutagenic, non-carcinogenic, non-cytotoxic, and non-hepatotoxic. During the 120 ns MD simulations, the RMSF analysis of galloylastragalin- MDM2 (complex 1) and galloylastragalin- Bcl-xL (complex 2) showed the fewest fluctuations, indicating the conformational stability of the respective complexes.

Conclusion: This study has shown that polyphenol compounds of E. uniflora led by galloylastragalin, are potent inhibitors of the MDM2 and Bcl-xL cancer proteins. Thus, they may be considered as candidate polyphenols for further anticancer studies.

Background: Ajuga integrifolia (Armagusa) is used as a decoction to treat high blood pressure and diabetes, widely in Ethiopia. Specific compounds for anti-hypertension activity were not identified so far. This study aims to provide a scientific basis for the therapeutic use of A. integrifolia as an antihypertension agent.

Methods: In silico studies were used to evaluate the antihypertensive components of A. integrifolia. Flavonoids identified using HPLC analysis and iridoid glycosides isolated from A. integrifolia in this study and those isolated from synonyms (A. remota and A. bractosa) were considered in the molecular docking study. Interactions were studied by using Autodock vina (1.2) on PyRx 0.8 and visualizing in 2D and 3D using ligPlot+ and Discovery studio software. Activities like vasoprotection and druglikeness properties were predicted using online servers.

Results: Flavonoids such as quercetin, myricetin, and rutin were identified and quantified by HPLC analysis from different extracts of A. integrifolia. Reptoside and 8-O-acetylharpgide isolated from the aerial part of A. integrifolia. The binding energies of all 17 candidates considered in this study range from -10.2 kcal/mol to -7.5 kcal/mol and are lower than enalapril (reference drug: -5.9 kcal/mol). The binding energies, in most case, constitute hydrogen bonding. Biological activity predicted using PASS test also showed that the flavonoids have more probability of activity than the iridoid glycosides. Druglikeness properties of the candidate molecules showed that most follow the Lipinski rule of five with few violations.

Conclusion: Lower binding energies involving hydrogen bonding and predicted activities concerning hypertension confirm the traditional use of the aerial part of the medicinal plant concerned. Flavonoids: rutin, myricetin, quercetin, and kaempferol take the leading role in the antihypertensive activity of the aerial part of A. integrifolia. The iridoid glycosides studied are almost similar in their effect on their antihypertensive activity and still better than the reference drug.

Background: Multidrug resistance in various cancer types is a major obstacle in cancer treatment. The concept of a single drug molecular target often causes treatment failure due to the complexity of the cellular processes. Therefore, combination chemotherapy, in which two or more anticancer drugs are co-administered, can overcome this problem because it potentially have synergistic efficacy besides reducing resistance, and drug doses. Previously, we reported that pyrazoline B had promising anticancer activity in both in silico and in vitro studies. To increase the efficacy of this drug, co-administration with established anticancer drugs such as doxorubicin and paclitaxel is necessary.

Materials and methods: In this study, we used an in silico approach to predict the synergistic effect of pyrazoline B with paclitaxel or doxorubicin using various computational frameworks and compared the results with those of an established study on the combination of doxorubicin-cyclophosphamide and paclitaxel-ascorbic acid.

Results and discussion: Drug interaction analysis showed the combination was safe with no contraindications or side effects. Furthermore, molecular docking studies revealed that doxorubicin-pyrazoline B and doxorubicin-cyclophosphamide may synergistically inhibit cancer cell proliferation by inhibiting the binding of topoisomerase I to the DNA chain. Moreover, the combination of pyrazoline B-paclitaxel may has synergistic activity to cause apoptosis by inhibiting Bcl2 binding to the Bax fragment or inhibiting cell division by inhibiting α-β tubulin disintegration. Paclitaxel-ascorbic acid had a synergistic effect on the inhibition of α-β tubulin disintegration.

Conclusion: The results show that this combination is promising for further in vitro and in vivo studies.

Background: Glyoxalase system detoxifies methylglyoxal and other ketoaldehydes to produce innocuous metabolites that allow the cells to function normally. Its inhibition in cancer cells causes these toxic metabolites to accumulate, and the cancer cells enter the apoptotic stage.

Methods: The techniques of Computer-Aided Drug Design (CADD) were used, and the compounds possessing a zinc-binding group from commercial databases were extracted, using the pharmacophore search protocol. These compounds were subjected to robust docking using the CDOCKER protocol within the Discovery Studio. Docking was performed on both Glo-I twin active sites. The biological activities of candidate hits were assessed using an in vitro assay against Glo-I.

Results: Compounds containing zinc-binding groups were extracted from ASINEX^® commercial database, which contains (91,001 compounds). This step has helped to retrieve 1809 ligands, which then were prepared and docked at the two active sites of Glo-I. The fourteen compounds, which have showed the highest scores in docking and returned acceptable Total Binding Energy values, were purchased and tested against the enzyme in vitro. Two compounds out of the fourteen, which were selected in the final step, possess tetrazole ring as zinc chelating moiety, and have showed moderate activity with an IC₅₀ of 48.18µM for SYN 25285236 and 48.77 µM for SYN 22881895.

Conclusion: Two hits with moderate activity are identified as the lead compounds against Glo-I. Both compounds possess a negatively ionized tetrazole ring as the zinc-binding moiety. These compounds will lead to the development of inhibitors with improved activities.