MLASM: Machine learning based prediction of anticancer small molecules.

Cancer, being the second leading cause of death globally. So, the development of effective anticancer treatments is crucial in the field of medicine. Anticancer peptides (ACPs) have shown promising therapeutic potential in cancer treatment compared to traditional methods. However, the process of identifying ACPs through experimental means is often time-intensive and expensive. To overcome this issue, we employed a machine learning-based approach for the first time to develop an anticancer model using small molecules. Anticancer small molecules (ACSMs) are compounds that have been developed to target and inhibit cancer cells. In this study, we used 10,000 compounds to develop the machine learning models using five algorithms such as, Random Forest (RF), Light gradient boosting machine (LightGBM), K-nearest neighbors (KNN), Decision tree (DT) and Extreme Gradient Boosting (XGB). The developed models were evaluated using the test set and top three models were identified (RF, LightGBM and XGB). Furthermore, to validate the predictive performance of our models, we have performed external validation using an FDA approved anticancer compounds/drugs. Following this analysis, we found that our LightGBM model correctly predicted 9 compounds as active. However, RF and XGB exhibited some limitations by predicting 8 and 7 compounds as active out of 10, respectively. These results demonstrate that, when compared to RF and XGB, the LightGBM model showcase robust prediction capabilities, achieving a superior accuracy of 79% with an AUC of 0.88. These findings provide promising insights into the potential of our approach for predicting anticancer small molecules, highlighting the role of machine learning in advancing cancer treatment research.

Computational study of the motor neuron protein KIF5A to identify nsSNPs, bioactive compounds, and its key regulators.

Introduction: Kinesin family member 5A (KIF5A) is a motor neuron protein expressed in neurons and involved in anterograde transportation of organelles, proteins, and RNA. Variations in the KIF5A gene that interfere with axonal transport have emerged as a distinguishing feature in several neurodegenerative disorders, including hereditary spastic paraplegia (HSP10), Charcot-Marie-Tooth disease type 2 (CMT2), and Amyotrophic Lateral Sclerosis (ALS). Methods: In this study, we implemented a computational structural and systems biology approach to uncover the role of KIF5A in ALS. Using the computational structural biology method, we explored the role of non-synonymous Single Nucleotide Polymorphism (nsSNPs) in KIF5A. Further, to identify the potential inhibitory molecule against the highly destabilizing structure variant, we docked 24 plant-derived phytochemicals involved in ALS. Results: We found KIF5AS291F variant showed the most structure destabilizing behavior and the phytocompound "epigallocatechin gallate" showed the highest binding affinity (-9.0 Kcal/mol) as compared to wild KIF5A (-8.4 Kcal/mol). Further, with the systems biology approach, we constructed the KIF5A protein-protein interaction (PPI) network to identify the associated Kinesin Families (KIFs) proteins, modules, and their function. We also constructed a transcriptional and post-transcriptional regulatory network of KIF5A. With the network topological parameters of PPIN (Degree, Bottleneck, Closeness, and MNC) using CytoHubba and computational knock-out experiment using Network Analyzer, we found KIF1A, 5B, and 5C were the significant proteins. The functional modules were highly enriched with microtubule motor activity, chemical synaptic transmission in neurons, GTP binding, and GABA receptor activity. In regulatory network analysis, we found KIF5A post-transcriptionally down-regulated by miR-107 which is further transcriptionally up-regulated by four TFs (HIF1A, PPARA, SREBF1, and TP53) and down-regulated by three TFs (ZEB1, ZEB2, and LIN28A). Discussion: We concluded our study by finding a crucial variant of KIF5A and its potential therapeutic target (epigallocatechin gallate) and KIF5A associated significant genes with important regulators which could decrypt the novel therapeutics in ALS and other neurodegenerative diseases.

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches.

JAK1 plays a significant role in the intracellular signaling by interacting with cytokine receptors in different types of cells and is linked to the pathogenesis of various cancers and in the pathology of the immune system. In this study, ligand-based pharmacophore modeling combined with virtual screening and molecular docking methods was incorporated to identify the potent and selective lead compounds for JAK1. Initially, the ligand-based pharmacophore models were generated using a set of 52 JAK1 inhibitors named C-2 methyl/hydroxyethyl imidazopyrrolopyridines derivatives. Twenty-seven pharmacophore models with five and six pharmacophore features were generated and validated using potency and selectivity validation methods. During potency validation, the Guner-Henry score was calculated to check the accuracy of the generated models, whereas in selectivity validation, the pharmacophore models that are capable of identifying selective JAK1 inhibitors were evaluated. Based on the validation results, the best pharmacophore models ADHRRR, DDHRRR, DDRRR, DPRRR, DHRRR, ADRRR, DDHRR, and ADPRR were selected and taken for virtual screening against the Maybridge, Asinex, Chemdiv, Enamine, Lifechemicals, and Zinc database to identify the new molecules with novel scaffold that can bind to JAK1. A total of 4,265 hits were identified from screening and checked for acceptable drug-like properties. A total of 2,856 hits were selected after ADME predictions and taken for Glide molecular docking to assess the accurate binding modes of the lead candidates. Ninety molecules were shortlisted based on binding energy and H-bond interactions with the important residues of JAK1. The docking results were authenticated by calculating binding free energy for protein-ligand complexes using the MM-GBSA calculation and induced fit docking methods. Subsequently, the cross-docking approach was carried out to recognize the selective JAK1 lead compounds. Finally, top five lead compounds that were potent and selective against JAK1 were selected and validated using molecular dynamics simulation. Besides, the density functional theory study was also carried out for the selected leads. Through various computational studies, we observed good potency and selectivity of these lead compounds when compared with the drug ruxolitinib. Compounds such as T5923555 and T5923531 were found to be the best and can be further validated using in vitro and in vivo methods.

Structure and dynamics of the somatostatin receptor 3-ligand binding in the presence of lipids examined using computational structural biology methods.

In the past two decades, the structural biology studies on G-protein coupled receptors (GPCRs) are on the rise. Understanding the relation between the structure and function of GPCRs is important as they play a huge role in various signaling mechanisms in a eukaryotic cell. Somatostatin receptor 3 (SSTR3), one of the GPCRs, is one such important receptor which oversees different cellular processes including cell-to-cell signaling. However, the information available regarding the structural features of SSTR3 responsible for their bioactivity is scarce. In this study, we report a structural understanding of SSTR3-ligand binding that could be helpful in demystifying the structural complexities related to functioning of the receptor. An integrated protocol consisting of different computational structural biology tools including protein structure prediction via comparative modeling, binding site characterization, three-dimensional quantitative structure-activity relationship based on comparative molecular field analysis and comparative molecular similarity indices analysis, density functional theory, and molecular dynamics simulations were performed. Different understandings from the simulation of SSTR3-ligand complexes, mainly the conditions that are favorable for the formation of lowest bioactive state of SSTR3 ligands are reported. In addition to that, we report the important physicochemical descriptors of SSTR3 ligands that could significantly influence their bioactivity. The results of the study could be helpful in developing novel SSTR3 ligands (both agonists and antagonists) with high potency and receptor selectivity.

Characterization of Phytochemicals in Ulva intestinalis L. and Their Action Against SARS-CoV-2 Spike Glycoprotein Receptor-Binding Domain.

Coronavirus disease-2019 (COVID-19) has caused a severe impact on almost all aspects of human life and economic development. Numerous studies are being conducted to find novel therapeutic strategies to overcome COVID-19 pandemic in a much effective way. Ulva intestinalis L. (Ui), a marine microalga, known for its antiviral property, was considered for this study to determine the antiviral efficacy against severe acute respiratory syndrome-associated Coronavirus-2 (SARS-CoV-2). The algal sample was dried and subjected to ethanolic extraction, followed by purification and analysis using gas chromatography-coupled mass spectrometry (GC-MS). Forty-three known compounds were identified and docked against the S1 receptor binding domain (RBD) of the spike (S) glycoprotein. The compounds that exhibited high binding affinity to the RBD of S1 protein were further analyzed for their chemical behaviour using conceptual density-functional theory (C-DFT). Finally, pharmacokinetic properties and drug-likeliness studies were carried out to test if the compounds qualified as potential leads. The results indicated that mainly phenols, polyenes, phytosteroids, and aliphatic compounds from the extract, such as 2,4-di-tert-butylphenol (2,4-DtBP), doconexent, 4,8,13-duvatriene-1,3-diol (DTD), retinoyl-ß-glucuronide 6',3'-lactone (RBGUL), and retinal, showed better binding affinity to the target. Pharmacokinetic validation narrowed the list to 2,4-DtBP, retinal and RBGUL as the possible antiviral candidates that could inhibit the viral spike protein effectively.

Antiviral Essential Oil Components Against SARS-CoV-2 in Pre-procedural Mouth Rinses for Dental Settings During COVID-19: A Computational Study.

COVID-19 mainly spreads through cough or sneeze droplets produced by an infected person. The viral particles are mostly present in the oral cavity. The risk of contracting COVID-19 is high in the dental profession due to the nature of procedures involved that produce aerosols. Along with other measures to limit the risk of infection, pre-procedural mouth rinses are beneficial in reducing the viral particles in the oral cavity. In this study, the antiviral efficacy of essential oil components has been determined specifically against SARS-CoV-2 by molecular docking and conceptual DFT approach. Based on the binding affinities of the components against the receptor binding domain of the S1 glycoprotein, cuminal, carvacrol, myrtanol, and pinocarveol were found to be highly active. The molecular descriptor values obtained through conceptual DFT also indicated the above-mentioned components to be active based on the correlation between the structure and the activity of the compounds. Therefore, pre-procedural mouth rinses with these components included may be specifically suitable for dental procedures during the COVID-19 period.

Understanding the influence of lipid bilayers and ligand molecules in determining the conformational dynamics of somatostatin receptor 2.

Somatostatin receptor 2 (SSTR2) is a G-protein coupled receptor (GPCR) that controls numerous cellular processes including cell-to-cell signaling. In this study, we report how the lipid and ligand molecules influence the conformational dynamics of the membrane-bound SSTR2. Molecular simulations of different holo and apoenzyme complexes of SSTR2 in the presence and absence of a lipid bilayer were performed, observed, and correlated with previously reported studies. We identified the important SSTR2 residues that take part in the formation of the SSTR2-ligand complex. On analyzing the molecular simulation trajectories, we identified that the residue D3.32 is crucial in determining the bioactive conformation of SSTR2 ligands in the binding site. Based on the results, we suggest that designing a novel SSTR2 ligand with an H-bond donor group at the R1 position, and hydrophobic groups at R2 and R3 might have higher activity and SSTR2-selectivity. We analyzed the simulated systems to identify other important structural features involved in SSTR2-ligand binding and to observe the different conformational changes that occur in the protein after the ligand binding. Additionally, we studied the conformational dynamics of N- and C-terminal regions of SSTR2 in the presence and absence of the lipid bilayer. Both the systems were compared to understand the influence of lipid molecules in the formation of secondary structural domains by these extracellular regions. The comparative study revealed that the secondary structural elements formed by C-terminal residues in presence of lipid molecules is crucial for the functioning of SSTR2. Our study results highlight the structural complexities involved in the functioning of SSTR upon binding with the ligands in the presence and absence of lipid bilayer, which is essential for designing novel drug targets.

C-Reactive Protein Sensor Based on Porous Silicon Bragg Stack Interferometers.

Bio-functionalized nanomaterials represent the cutting-edge research for a sensing biomolecules in nano-systems. Their physicochemical properties of porous silicon bring along advantages in sensing applications. Here, a modified biosensor based on the anti-C-reactive protein-modified porous silicon Bragg stack interferometer was developed to detect C-reactive protein. The SEM images of the surface and cross-sectional views of the Bragg stack porous silicon exhibited the pore sizes in the 10-20 nm range. The fabrication, optical characterization, and surface derivatization of the interferometer were also reported. This sensor was assessed by measuring the reflection peaks in the white light reflection spectrum. As a result, molecular binding was detected as a shift in the wavelength of these reflection peaks. In addition, a dramatic decrease in the reflectivity was observed in the reflectivity spectrum within 10 s, thereby indicating a C-reactive protein detection limit of 100 pM.

Fast Curable Polysiloxane-Silphenylene Hybrimer with High Transparency and Refractive Index for Optical Applications.

In this study, a fast curable polysiloxane-silphenylene hybrimer (PSH) was synthesized by the nonhydrolytic sol-gel condensation of phenyl-vinyl-oligosiloxane (PVO) and tris(dimethylhydrosilyl)benzene (TDMSB) under a Pt catalyst to investigate its optical property and thermal stability. The combination of PVO and tripod-type TDMSB results in a hybrimer with a fast curing time of 30 min. The PSH exhibited a high refractive index of 1.60, 1.59, and 1.58 at 450, 520, and 635 nm, respectively. High transmittance of 97% at 450 nm was obtained. The PSH exhibited a very high transmittance of 97% before thermal aging. The high optical transmittance of the PSH was only slightly decreased by 0.5% of the transmittance at 180 °C for 72 h after thermal aging, and high transparency was maintained almost constant even after 72 h of high-temperature treatment.

Molecular-Level Understanding of the Somatostatin Receptor 1 (SSTR1)-Ligand Binding: A Structural Biology Study Based on Computational Methods.

Somatostatin receptor 1 (SSTR1), a subtype of somatostatin receptors, is involved in various signaling mechanisms in different parts of the human body. Like most of the G-protein-coupled receptors (GPCRs), the available information on the structural features of SSTR1 responsible for the biological activity is scarce. In this study, we report a molecular-level understanding of SSTR1-ligand binding, which could be helpful in solving the structural complexities involved in SSTR1 functioning. Based on a three-dimensional quantitative structure-activity relationship (3D-QSAR) study using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA), we have identified that an electronegative, less-bulkier, and hydrophobic atom substitution can substantially increase the biological activity of SSTR1 ligands. A density functional theory (DFT) study has been followed to study the electron-related properties of the SSTR1 ligands and to validate the results obtained via the 3D-QSAR study. 3D models of SSTR1-ligand systems have been embedded in lipid-lipid bilayer membranes to perform molecular dynamics (MD) simulations. Analysis of the MD trajectories reveals important information about the crucial residues involved in SSTR1-ligand binding and various conformational changes in the protein that occur after ligand binding. Additionally, we have identified the probable ligand-binding site of SSTR1 and validated it using MD. We have also studied the favorable conditions that are essential for forming the most stable and lowest-energy bioactive conformation of the ligands inside the binding site. The results of the study could be useful in constructing more potent and novel SSTR1 antagonists and agonists.

A Novel Chemical Gas Vapor Sensor Based on Photoluminescence Enhancement of Rugate Porous Silicon Filters.

In this study, an innovative rugate filter configuration porous silicon (PSi) with enhanced photoluminescence intensity was fabricated. The fabricated PSi exhibited dual optical properties with both sharp optical reflectivity and sharp photoluminescence (PL), and it was developed for use in organic vapor sensing. When the wavelength of the resonance peak from the rugate PSi filters is engineered to overlap with the emission band of the PL from the PSi quantum dots, the PL intensity is amplified, thus reducing the full width at half maximum (FWHM) of the PL band from 154 nm to 22 nm. The rugate PSi filters samples were fabricated by electrochemical etching of highly doped n-type silicon under illumination. The etching solution consisted of a 1:1 volume mixture of 48% hydrofluoric acid and absolute ethanol and photoluminescent rugate PSi filter was fabricated by etching while using a periodic sinusoidal wave current with 10 cycles. The obtained samples were characterized by scanning electron microscopy (SEM), and both reflection redshift and PL quenching were measured under exposure to organic vapors. The reflection redshift and PL quenching were both affected by the vapor pressure and dipole moment of the organic species.

Heavy Atom Effect on Metallole Nanoaggregates and Their Explosive Sensing Applications.

Electronic and optical properties of metallole nanoaggregates (M = Ge and Sn) were investigated. Amplified photoluminescence (PL) properties, absolute quantum yields (QY), and critical aggregation concentrations for the metallole nanoaggregates were measured. The an aggregation-induced emission enhancement (AIEE) property decreases as the central atom becomes heavier in the metallole ring. Detection of TNT was achieved by the quenching PL of the metallole nanoaggregates. A linear Stern-Volmer relationship was observed for the detection of TNT.

Oxidized Porous Silicon Nanoparticles Covalent-Bonded with Levofloxacin in Hydrogel Polymer as a Drug Delivery System.

Oxidized porous silicon (OPS) nanoparticles covalent-bonded with levofloxacin in hydrogel polymer are prepared and used to measure the efficiency for the controlled release drug delivery. Levofloxacin is covalently bonded to a Si-OH surface of OPS nanoparticles by catalytic condensation. The average size of these particles is about 100 nm. Since levofloxacin has a fluorescence property, the release of levofloxacin has been measured by fluorescence spectrometer. The quantity of levofloxacin release from the OPS nanoparticles is measured at the emission wavelength at 455 nm with an excitation wavelength of 330 nm on a time scale in pH 7 aqueous buffer solution. The analysis of release profile reveals that the OPS covalently bonded with levofloxacin (Levo-OPS) exhibits a great potential candidate for controlled release. The drug-release rate depends on the hydrolysis of silyl ester from the surface of OPS nanoparticles.

Water Soluble Silicon Quantum Dots Grafted with Amoxicillin as a Drug Delivery System.

Water soluble silicon quantum dots (Si QDs) are prepared and used for the measurement of the efficiency for the controlled release drug delivery. Amoxicillin, antibiotics, is covalently grafted with Si QDs through a surface derivatization reaction. Si QDs are embedded in hydrogel polymer. The release of amoxicillin has been measured by using Ultra violet-visible (UV-vis) absorption spectrometer. Amoxicillin-embedded hydrogels exhibit a burst release for 1 h, however covalently loaded Amoxi-Si QDs hydrogel composite exhibited very slow release. Absorption analysis reveals that the Si QDs exhibits a great potential candidate for controlled release of drug. The controlled drugrelease profiles depend on the hydrolysis of amoxicillin from the surface of Si QDs.

Controlled nanoparticle synthesis of Ag/Fe co-doped hydroxyapatite system for cancer cell treatment.

Diagnosis of cancer by chemotherapy treatment, severe side effects caused by high dosages of cancer drugs include non-controlled cytotoxicity to bone marrow cells and immune cells. To overcome, we have synthesized nanoparticles with controlled sized hydroxyapatite (nHAp) materials doped and co-doped with silver and iron by co-precipitation, yielding materials that can treat both the infections and malignant tumors with non-cytotoxic nature to normal cells. Spherical and rod like morphologies were observed for the samples with higher Ag+ doping concentrations with average size of 50 ±â€¯5 nm and (75 × 22) ±â€¯5 nm2, whereas higher Ag+/Fe2+ co-doping concentrations yielded samples with spherical, rod-like, and flake-like structures. For samples nHAp and Ag+-nHAp samples were diamagnetic, whereas the Fe2+-nHAp and Ag+/Fe2+ co-doped samples were superparamagnetic. The in vitro biological toxicity study revealed that the Ag+/Fe2+-nHAp nanoparticles are effective for targeting to kill cancerous cells, for example, human cervical cancer (HeLa) cells efficiently while they are non-toxic to normal cells. Applying these nanoparticles for drug delivery system, 5-fluorouracil was loaded in the nanoparticles and studied its release kinetics. In the case of Ag+/Fe2+co-doped nHAp samples, a pulsatile drug release profile was observed, which the drug was released for about a week on varying the Ag+ and Fe2+ concentrations. The 5-fluorouracil release kinetics was well fitted by the first-order model with diffusion. Thus, nHAps co-doped with Ag+/Fe2+ material have the potential to lag the time on delivering the drug at site-specific could be with an application in biomedicine such as to treat malignant tumor without any bacterial side effect.

Toward a better understanding of the interaction between somatostatin receptor 2 and its ligands: a structural characterization study using molecular dynamics and conceptual density functional theory.

This study is a part of the extensive research intending to provide the structural insights on somatostatin and its receptor. Herein, we have studied the structural complexity involved in the binding of somatostatin receptor 2 (SSTR2) with its agonists and antagonist. A 3D QSAR study based on comparative molecular field analysis and comparative molecular similarity analysis (CoMSIA) discerned that a SSTR2 ligand with electronegative, less-bulkier, and hydrogen atom donating/accepting substitutions is important for their biological activity. A conceptual density functional theory (DFT) study was followed to study the chemical behavior of the ligands based on the molecular descriptors derived using the Fukui's molecular orbital theory. We have performed molecular dynamics simulations of receptor-ligand complexes for 100 ns to analyze the dynamic stability of the backbone Cα atoms of the receptor and strength and approachability of the receptor-ligand complex. The findings of this study could be efficacious in the further studies understanding intricate structural features of the somatostatin receptors and in discovering novel subtype-specific ligands with higher affinity. Communicated by Ramaswamy H. Sarma.

1,3,5-Trinitrotoluene Sensor Based on Silole Nanoaggregates.

Bis(methyltetraphenyl)silole and bis(methyltetraphenyl)silole siloxane nanoaggregates for the detection of TNT were developed by using aggregation-induced emission property. Their absolute quantum yields and critical water concentration for onset of aggregation were measured. Average particle size for both nanoaggregates were measured and tuned by controlling the water fraction by volume. Absolute quantum yield of both nanoaggregates in 90% water volume fraction increased by more than 40 times. Detection of TNT was achieved from the quenching PL measurement of both nanoaggregates by adding the TNT. A linear Stern-Volmer relationship was observed for the detection of TNT.

3D-QSAR studies on indole and 7-azoindole derivatives as ROCK-2 inhibitors: An integrative computational approach.

Rho Kinases (ROCK) has been found to regulate a wide range of fundamental cell functions such as contraction, motility, proliferation, and apoptosis. Recent experiments have defined new functions of ROCKs in cells, including centrosome positioning and cell-size regulation, which might contribute to various physiological and pathological states. In this study, we have performed pharmacophore modeling and 3D QSAR studies on a series of 36 indoles and 7-azoindoles derivatives as ROCK2 inhibitors to elucidate the structural variations with their inhibitory activities. Ligand based CoMFA and CoMSIA models were generated based on three different alignment methods such as systematic search, simulated annealing and pharmacophore. A total of 15 CoMFA models and 27 CoMSIA were generated using different alignments. One model from each alignment is selected based on the statistical values. Contour maps of the selected models were compared, analysed and reported. The 3D QSAR study revealed that electro positive group linked to the methoxy-benzene ring position of the structure will enhance the biological activity and bulkier substitutions are preferred in the methyl dihydroindole region. Also, it is found that the hydrogen bond donor substituted at the R1 position enhances the inhibitory activity. In future, this study would give proper guidelines to further enhance the activity of novel inhibitors for ROCK2.

Silicon quantum dot sensors for an explosive taggant, 2,3-dimethyl-2,3-dinitrobutane (DMNB).

Silicon quantum dots obtained by the reaction of magnesium silicide with ethylenediamine dihydrochloride were utilized to investigate the sensing mechanism and sensitivity for DMNB detection applications. Sensing DMNB provided us with evidence that Si QDs with a higher lying conduction band have better sensitivity compared to CdSe QDs.

Molecular Modeling Study on Diazine Indole Acetic Acid Derivatives for CRTH2 Inhibitory Activity.

In the present work, molecular modeling studies have been reported on a series of diazine indole acetic acid derivatives to analyze the structure-activity relationship studies of CRTH2 using fragment (Topomer CoMFA and HQSAR) and field (CoMFA and CoMSIA) based QSAR methods. Twenty-six compounds were used as a training set to establish the model, and six compounds were used as a test set to validate the model. The generated models exhibited good statistical results such as correlation coefficient (r2) and the cross-validated correlation coefficient (q2). Topomer CoMFA analysis yielded the q2 of 0.610 and r2 of 0.981. HQSAR model generated using bond and connectivity as fragment distinction and 3-6 as fragment size has the q2 value of 0.707 and conventional r2 value of 0.892 with five components. CoMFA model was assessed by cross-validated q2 value of 0.543 and r2 value of 0.901 with steric and electrostatic fields. CoMSIA model generated using steric, hydrophobic and donor fields with q2 value of 0.550 and r2 value of 0.888 was found to be the optimal model among the various models generated. The contour maps were generated to analyze the important structural features that regulate their inhibitory potency. From the result of contour maps we have suggested the critical sites for chemical modification which will be useful in designing potent compounds with improved activity.