Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors.

Activity cliffs (ACs) are pairs of structurally similar molecules with significantly different affinities for a biotarget, posing a challenge in computer-assisted drug discovery. This study focuses on protein kinases, significant therapeutic targets, with some exhibiting ACs while others do not despite numerous inhibitors. The hypothesis that the presence of ACs is dependent on the target protein and its complete structural context is explored. Machine learning models were developed to link protein properties to ACs, revealing specific tripeptide sequences and overall protein properties as critical factors in ACs occurrence. The study highlights the importance of considering the entire protein matrix rather than just the binding site in understanding ACs. This research provides valuable insights for drug discovery and design, paving the way for addressing ACs-related challenges in modern computational approaches.

Preparation of novel shell-ionotropically crosslinked micelles based on hexadecylamine and tripolyphosphate for cancer drug delivery.

AIMS: Micellar systems have the advantage of being easily prepared, cheap, and readily loadable with bioactive molecular cargo. However, their fundamental pitfall is poor stability, particularly under dilution conditions. We propose to use simple quaternary ammonium surfactants, namely, hexadecylamine (HDA) and hexadecylpyridinium (HDAP), together with tripolyphosphate (TPP) anion, to generate ionotropically stabilized micelles capable of drug delivery into cancer cells. METHODS: optimized mixed HDA/HDAP micelles were prepared and stabilized with TPP. Curcumin was used as a loaded model drug. The prepared nanoparticles were characterized by dynamic light scattering, infrared spectroscopy, transmission electron microscopy, and differential scanning calorimetry. Moreover, their cellular uptake was assessed using flow cytometry and confocal fluorescence microscopy. RESULTS: The prepared nanoparticles were found to be stable under dilution and at high temperatures and to have a size range from 139 nm to 580 nm, depending on pH (4.6-7.4), dilution (up to 100 times), and temperature (25 - 80 °C). They were effective at delivering their load into cancer cells. Additionally, flow cytometry indicated the resulting stabilized micellar nanoparticles to be non-cytotoxic. CONCLUSIONS: The described novel stabilized micelles are simple to prepare and viable for cancer delivery.

Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes.

Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.

Synthesis and antitumor activity of model cyclopentene-[g]annelated isoindigos.

A set of cyclopenten-[g]annelated isoindigos (5a-g) has been prepared and tested for their in vitro antiproliferative activities against MCF-7 and HL60 cells. Among, the N-1-methyl-5'-nitro derivative (5g) displayed the highest activity against HL60 cells (IC50 = 67 nM) and acted as the most potent Flt3 inhibitor. Compounds 5d-g exhibited moderate activity against MCF-7 (IC50 = 50-80 µM).

Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study.

STAT3 belongs to a family of seven transcription factors. It plays an important role in activating the transcription of various genes involved in a variety of cellular processes. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. However, since STAT3 inhibitors bind to the shallow SH2 domain of the protein, it is expected that hydration water molecules play significant role in ligand-binding complicating the discovery of potent binders. To remedy this issue, we herein propose to extract pharmacophores from molecular dynamics (MD) frames of a potent co-crystallized ligand complexed within STAT3 SH2 domain. Subsequently, we employ genetic function algorithm coupled with machine learning (GFA-ML) to explore the optimal combination of MD-derived pharmacophores that can account for the variations in bioactivity among a list of inhibitors. To enhance the dataset, the training and testing lists were augmented nearly a 100-fold by considering multiple conformers of the ligands. A single significant pharmacophore emerged after 188 ns of MD simulation to represent STAT3-ligand binding. Screening the National Cancer Institute (NCI) database with this model identified one low micromolar inhibitor most likely binds to the SH2 domain of STAT3 and inhibits this pathway.

Augmenting bioactivity by docking-generated multiple ligand poses to enhance machine learning and pharmacophore modelling: discovery of new TTK inhibitors as case study.

Dual specificity protein kinase threonine/Tyrosine kinase (TTK) is one of the mitotic kinases. High levels of TTK are detected in several types of cancer. Hence, TTK inhibition is considered a promising therapeutic anti-cancer strategy. In this work, we used multiple docked poses of TTK inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contacts Fingerprints and docking scoring values were used as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to determine critical descriptors for predicting anti-TTK bioactivity and for pharmacophore generation. Three successful pharmacophores were deduced and subsequently used for in silico screening against the NCI database. A total of 14 hits were evaluated in vitro for their anti-TTK bioactivities. One hit of novel chemotype showed reasonable dose-response curve with experimental IC50 of 1.0 µM. The presented work indicates the validity of data augmentation using multiple docked poses for building successful machine learning models and pharmacophore hypotheses.

Molecular docking, molecular dynamics simulations and in vitro screening reveal cefixime and ceftriaxone as GSK3ß covalent inhibitors.

GSK3ß is a serine/threonine kinase that has been suggested as a putative drug target for several diseases. Recent studies have reported the beneficial effects of cephalosporin antibiotics in cancer and Alzheimer's disease, implying potential inhibition of GSK3ß. To investigate this mechanism, four cephalosporins, namely, cefixime, ceftriaxone, cephalexin and cefadroxil were docked into the GSK3ß binding pocket. The third-generation cephalosporins, cefixime and ceftriaxone, exhibited the best docking scores due to the exclusive hydrogen bonding between their aminothiazole group and hinge residues of GSK3ß. The stability of top-ranked poses and the possibility of covalent bond formation between the carbonyl carbon of the ß-lactam ring and the nucleophilic thiol of Cys-199 were evaluated by molecular dynamics simulations and covalent docking. Finally, the in vitro inhibitory activities of the four cephalosporins were measured against GSK3ß with and without preincubation. In agreement with the results of molecular docking, cefixime and ceftriaxone exhibited the best inhibitory activities with IC50 values of 2.55 µM and 7.35 µM, respectively. After 60 minutes preincubation with GSK3ß, the IC50 values decreased to 0.55 µM for cefixime and 0.78 µM for ceftriaxone, supporting a covalent bond formation as suggested by molecular dynamics simulations and covalent docking. In conclusion, the third-generation cephalosporins are reported herein as GSK3ß covalent inhibitors, offering insight into the mechanism behind their benefits in cancer and Alzheimer's disease.

Discovery of new STAT3 inhibitors as anticancer agents using ligand-receptor contact fingerprints and docking-augmented machine learning.

STAT3 belongs to a family of seven vital transcription factors. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. In this work, we used multiple docked poses of STAT3 inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contact Fingerprints and scoring values were implemented as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to identify critical descriptors that determine anti-STAT3 bioactivity to be translated into pharmacophore model(s). Two successful pharmacophores were deduced and subsequently used for in silico screening against the National Cancer Institute (NCI) database. A total of 26 hits were evaluated in vitro for their anti-STAT3 bioactivities. Out of which, three hits of novel chemotypes, showed cytotoxic IC50 values in the nanomolar range (35 nM to 6.7 µM). However, two are potent dihydrofolate reductase (DHFR) inhibitors and therefore should have significant indirect STAT3 inhibitory effects. The third hit (cytotoxic IC50 = 0.44 µM) is purely direct STAT3 inhibitor (devoid of DHFR activity) and caused, at its cytotoxic IC50, more than two-fold reduction in the expression of STAT3 downstream genes (c-Myc and Bcl-xL). The presented work indicates that the concept of data augmentation using multiple docked poses is a promising strategy for generating valid machine learning models capable of discriminating active from inactive compounds.

Vitamin B12 binding to mutated human transcobalamin, in-silico study of TCN2 alanine scanning and ClinVar missense mutations/SNPs.

Many missense mutations/SNPs of the TCN2 gene (which yield Transcobalamin (TC)) were reported in the literature but no study is available about their effect on binding to vitamin B12(B12) at the structural level experimentally nor computationally. Predict the effect of TC missense mutations/SNPs on binding affinity to B12 and characterize their contacts to B12 at the structural level. TC-B12 binding energy difference from the wildtype (ΔΔGmut) was calculated for 378 alanine scanning mutations and 76 ClinVar missense mutations, repeated on two distinct X-ray structures of holoTC namely 2BB5 and 4ZRP. Destabilizing mutations then went through 100 ns molecular dynamics simulation to study their effect on TC-B12 binding at the structural level employing 2BB5 structure. Out of the studied 454 mutations (378 alanine mutations + 76 ClinVar mutations), 19 were destabilizing representing 17 amino acid locations. Mutation energy results show a neutral effect on B12 binding of several missense SNPs reported in the literature including I23V, G94S, R215W, P259R, S348F, L376S, and R399Q. Compared to the wildtype, all the destabilizing mutations have higher average RMSD-Ligand in the last 25% of the MD simulation trajectories and lower average hydrogen bond count while the other parameters vary. Previously reported TCN2 SNPs with an unknown effect on TC-B12 binding were found to have a neutral effect in the current study based on mutation energy calculations. Also, we reported 17 possible amino acids that destabilize TC-B12 binding upon mutation (four listed in ClinVar) and studied their structural effect computationally.

Discovery of new PKN2 inhibitory chemotypes via QSAR-guided selection of docking-based pharmacophores.

Serine/threonine-protein kinase N2 (PKN2) plays an important role in cell cycle progression, cell migration, cell adhesion and transcription activation signaling processes. In cancer, however, it plays important roles in tumor cell migration, invasion and apoptosis. PKN2 inhibitors have been shown to be promising in treating cancer. This prompted us to model this interesting target using our QSAR-guided selection of docking-based pharmacophores approach where numerous pharmacophores are extracted from docked ligand poses and allowed to compete within the context of QSAR. The optimal pharmacophore was sterically-refined, validated by receiver operating characteristic (ROC) curve analysis and used as virtual search query to screen the National Cancer Institute (NCI) database for new promising anti-PKN2 leads of novel chemotypes. Three low micromolar hits were identified with IC50 values ranging between 9.9 and 18.6 µM. Pharmacological assays showed promising cytotoxic properties for active hits in MTT and wound healing assays against MCF-7 and PANC-1 cancer cells.

Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning.

Lysine-specific histone demethylase 1 (LSD-1) is an epigenetic enzyme that oxidatively cleaves methyl groups from monomethyl and dimethyl Lys4 of histone H3 and is highly overexpressed in different types of cancer. Therefore, it has been widely recognized as a promising therapeutic target for cancer therapy. Towards this end, we employed various Computer Aided Drug Design (CADD) approaches including pharmacophore modelling and machine learning. Pharmacophores generated by structure-based (SB) (either crystallographic-based or docking-based) and ligand-based (LB) (either supervised or unsupervised) modelling methods were allowed to compete within the context of genetic algorithm/machine learning and were assessed by Shapley additive explanation values (SHAP) to end up with three successful pharmacophores that were used to screen the National Cancer Institute (NCI) database. Seventy-five NCI hits were tested for their LSD-1 inhibitory properties against neuroblastoma SH-SY5Y cells, pancreatic carcinoma Panc-1 cells, glioblastoma U-87 MG cells and in vitro enzymatic assay, culminating in 3 nanomolar LSD-1 inhibitors of novel chemotypes.

Pharmacophore Modeling of Targets Infested with Activity Cliffs via Molecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study.

Activity cliffs (ACs) are defined as pairs of structurally similar compounds with large difference in their potencies against certain biotarget. We recently proposed that potent AC members induce significant entropically-driven conformational modifications of the target that unveil additional binding interactions, while their weakly-potent counterparts are enthalpically-driven binders with little influence on the protein target. We herein propose to extract pharmacophores for ACs-infested target(s) from molecular dynamics (MD) frames of purely "enthalpic" potent binder(s) complexed within the particular target. Genetic function algorithm/machine learning (GFA/ML) can then be employed to search for the best possible combination of MD pharmacophore(s) capable of explaining bioactivity variations within a list of inhibitors. We compared the performance of this approach with established ligand-based and structure-based methods. Kinase inserts domain receptor (KDR) was used as a case study. KDR plays a crucial role in angiogenic signalling and its inhibitors have been approved in cancer treatment. Interestingly, GFA/ML selected, MD-based, pharmacophores were of comparable performances to ligand-based and structure-based pharmacophores. The resulting pharmacophores and QSAR models were used to capture hits from the national cancer institute list of compounds. The most active hit showed anti-KDR IC50 of 2.76 µM.

Discovery of new Cdc2-like kinase 4 (CLK4) inhibitors via pharmacophore exploration combined with flexible docking-based ligand/receptor contact fingerprints and machine learning.

Cdc2-like kinase 4 (CLK4) inhibitors are of potential therapeutic value in many diseases particularly cancer. In this study, we combined extensive ligand-based pharmacophore exploration, ligand-receptor contact fingerprints generated by flexible docking, physicochemical descriptors and machine learning-quantitative structure-activity relationship (ML-QSAR) analysis to investigate the pharmacophoric/binding requirements for potent CLK4 antagonists. Several ML methods were attempted to tie these properties with anti-CLK4 bioactivities including multiple linear regression (MLR), random forests (RF), extreme gradient boosting (XGBoost), probabilistic neural network (PNN), and support vector regression (SVR). A genetic function algorithm (GFA) was combined with each method for feature selection. Eventually, GFA-SVR was found to produce the best self-consistent and predictive model. The model selected three pharmacophores, three ligand-receptor contacts and two physicochemical descriptors. The GFA-SVR model and associated pharmacophore models were used to screen the National Cancer Institute (NCI) structural database for novel CLK4 antagonists. Three potent hits were identified with the best one showing an anti-CLK4 IC50 value of 57 nM.

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors.

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.

Exploiting activity cliffs for building pharmacophore models and comparison with other pharmacophore generation methods: sphingosine kinase 1 as case study.

Activity cliffs (ACs) are defined as closely analogous compounds of significant affinity discrepancies against certain biotarget. In this paper we propose to use AC pair(s) for extracting valid binding pharmacophores through exposing corresponding protein complexes to stochastic deformation/relaxation followed by applying genetic algorithm/machine learning (GA-ML) for selecting optimal pharmacophore(s) that best classify a long list of inhibitors. We compared the performances of ligand-based and structure-based pharmacophores with counterparts generated by this newly introduced technique. Sphingosine kinase 1 (SPHK-1) was used as case study. SPHK-1 is a lipid kinase that plays pivotal role in the regulation of a variety of biological processes including, cell growth, apoptosis, and inflammation. The new approach proved to yield pharmacophore and ML models of comparable accuracies to established ligand-based and structure-based pharmacophores. The resulting pharmacophores and ML models were used to capture hits from the national cancer institute list of compounds and predict their bioactivity categories. Two hits of novel chemotypes showed selective and low micromolar inhibitory IC50 values against SPHK-1.

Ligand-based Modeling of CXC Chemokine Receptor 4 and Identification of Inhibitors of Novel Chemotypes as Potential Leads towards New Anti- COVID-19 Treatments.

BACKGROUND: Chemokines are involved in several human diseases and different stages of COVID-19 infection. They play a critical role in the pathophysiology of the associated acute respiratory disease syndrome, a major complication leading to death among COVID-19 patients. In particular, CXC chemokine receptor 4 (CXCR4) was found to be highly expressed in COVID-19 patients. METHODS: We herein describe a computational workflow based on combining pharmacophore modeling and QSAR analysis towards the discovery of novel CXCR4 inhibitors. Subsequent virtual screening identified two promising CXCR4 inhibitors from the National Cancer Institute (NCI) list of compounds. The most active hit showed in vitro IC50 value of 24.4 µM. CONCLUSION: These results proved the validity of the QSAR model and associated pharmacophore models as means to screen virtual databases for new CXCR4 inhibitors as leads for the development of new COVID-19 therapies.

Novel Ellipsoid Chitosan-Phthalate Lecithin Nanoparticles for siRNA Delivery.

Small interfering RNA (siRNA) has received increased interest as a gene therapeutic agent. However, instability and lack of safe, affordable, and effective carrier systems limit siRNA's widespread clinical use. To tackle this issue, synthetic vectors such as liposomes and polymeric nanoparticles have recently been extensively investigated. In this study, we exploited the advantages of reduced cytotoxicity and enhanced cellular penetration of chitosan-phthalate (CSP) together with the merits of lecithin (LC)-based nanoparticles (NPs) to create novel, ellipsoid, non-cytotoxic, tripolyphosphate (TPP)-crosslinked NPs capable of delivering siRNA efficiently. The resulting NPs were characterized by dynamic light scattering (DLS) and transmission electron microscopy (TEM), and were found to be ellipsoid in the shape of ca. 180 nm in size, exhibiting novel double-layer shells, with excellent stability at physiological pH and in serum solutions. MTT assay and confocal fluorescence microscopy showed that CSP-LC-TPP NPs are non-cytotoxic and efficiently penetrate cancer cells in vitro. They achieved 44% silencing against SLUG protein in MDA-MB-453 cancer cells and were significantly superior to a commercial liposome-based transfection agent that achieved only 30% silencing under comparable conditions. Moreover, the NPs protected their siRNA cargos in 50% serum and from being displaced by variable concentrations of heparin. In fact, CSP-LC-TPP NPs achieved 26% transfection efficiency in serum containing cell culture media. Real-time wide-field fluorescence microscopy showed siRNA-loaded CSP-LC-TPP NPs to successfully release their cargo intracellularly. We found that the amphoteric nature of chitosan-phthalate polymer promotes the endosomal escape of siRNA and improves the silencing efficiency.

Docking-generated multiple ligand poses for bootstrapping bioactivity classifying Machine Learning: Repurposing covalent inhibitors for COVID-19-related TMPRSS2 as case study.

In the present work we introduce the use of multiple docked poses for bootstrapping machine learning-based QSAR modelling. Ligand-receptor contact fingerprints are implemented as descriptor variables. We implemented this method for the discovery of potential inhibitors of the serine protease enzyme TMPRSS2 involved the infectivity of coronaviruses. Several machine learners were scanned, however, Xgboost, support vector machines (SVM) and random forests (RF) were the best with testing set accuracies reaching 90%. Three potential hits were identified upon using the method to scan known untested FDA approved drugs against TMPRSS2. Subsequent molecular dynamics simulation and covalent docking supported the results of the new computational approach.

Inhibition of aggregation of amyloid-ß through covalent modification with benzylpenicillin; potential relevance to Alzheimer's disease.

The pathogenesis of Alzheimer's disease (AD) is correlated with the misfolding and aggregation of amyloid-beta protein (Aß). Here we report that the antibiotic benzylpenicillin (BP) can specifically bind to Aß, modulate the process of aggregation and supress its cytotoxic effect, initially via a reversible binding interaction, followed by covalent bonding between specific functional groups (nucleophiles) within the Aß peptide and the beta-lactam ring. Mass spectrometry and computational docking supported covalent modification of Aß by BP. BP was found to inhibit aggregation of Aß as revealed by the Thioflavin T (ThT) fluorescence assay and atomic force microscopy (AFM). In addition, BP treatment was found to have a cytoprotective activity against Aß-induced cell cytotoxicity as shown by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) cell toxicity assay. The specific interaction of BP with Aß suggests the possibility of structure-based drug design, leading to the identification of new drug candidates against AD. Moreover, good pharmacokinetics of beta-lactam antibiotics and safety on long-time use make them valuable candidates for drug repurposing towards neurological disorders such as AD.