Unlocking translational machinery for antitubercular drug development.

Targeting translational factor proteins (TFPs) presents significant promise for the development of innovative antitubercular drugs. Previous insights from antibiotic binding mechanisms and recently solved 3D crystal structures of Mycobacterium tuberculosis (Mtb) elongation factor thermo unstable-GDP (EF-Tu-GDP), elongation factor thermo stable-EF-Tu (EF-Ts-EF-Tu), and elongation factor G-GDP (EF-G-GDP) have opened up new avenues for the design and development of potent antituberculosis (anti-TB) therapies.

Characterization of the enzymatic and multifunctional properties of Acinetobacter baumannii erythrose-4-phosphate dehydrogenase (E4PDH).

Infections due to Acinetobacter baumannii (A. baumannii) are rapidly increasing worldwide and consequently therapeutic options for treatment are limited. The emergence of multi drug resistant (MDR) strains has rendered available antibiotics ineffective, necessitating the urgent discovery of new drugs and drug targets. The vitamin B6 biosynthetic pathway has been considered as a potential antibacterial drug target but it is as yet uncharacterized for A. baumannii. In the current work, we have carried out in silico and biochemical characterization of Erythrose-4-phosphate dehydrogenase (E4PDH) (EC 1.2.1.72). This enzyme catalyzes the first step in the deoxyxylulose-5-phosphate (DXP) dependent Vitamin B6 biosynthetic pathway i.e. the conversion of d-erythrose-4-phosphate (E4P) to 4-Phosphoerythronate. E4PDH also possesses an additional activity whereby it can catalyze the conversion of Glyceraldehyde-3-phosphate (G3P) to 1,3 bisphosphoglycerate (1,3BPG). Our studies have revealed that this enzyme exhibits an alternate moonlighting function as a cell surface receptor for the human iron transport proteins transferrin (Tf) and lactoferrin (Lf). The present work reports the internalization of Tf and consequent iron acquisition as an alternate strategy for iron acquisition. Given its essential role in two crucial pathways i.e. metabolism and iron acquisition, A. baumannii E4PDH may play a vital role in bacterial pathogenesis.

Machine Learning Enables Accurate Prediction of Quinone Formation during Drug Metabolism.

Metabolism helps in the elimination of drugs from the human body by making them more hydrophilic. Sometimes, drugs can be bioactivated to highly reactive metabolites or intermediates during metabolism. These reactive metabolites are often responsible for the toxicities associated with the drugs. Identification of reactive metabolites of drug candidates can be very helpful in the initial stages of drug discovery. Quinones are soft electrophiles that are generated as reactive intermediates during metabolism. Quinones make up more than 40% of the reactive metabolites. In this work, a reliable data set of 510 molecules was used to develop machine learning and deep learning-based predictive models to predict the formation of quinone-type metabolites. For representing molecules, two-dimensional (2D) descriptors, PubChem fingerprints, electro-topological state (E-state) fingerprints, and metabolic reactivity-based descriptors were used. Developed models were compared to the existing Xenosite web server using the untouched test set of 102 molecules. The best model achieved an accuracy of 86.27%, while the Xenosite server could achieve an accuracy of only 52.94% on the test set. Descriptor analysis revealed that the presence of greater numbers of polar moieties in a molecule can prevent the formation of quinone-type metabolites. In addition, the presence of a nitrogen atom in an aromatic ring and the presence of metabolophores V51, V52, and V53 (SMARTCyp descriptors) decrease the probability of quinone formation. Finally, a tool based on the best machine learning models was developed, which is accessible at http://14.139.57.41/quinonepred/.

TNFipred: a classification model to predict TNF-α inhibitors.

Rheumatoid arthritis (RA), characterized by severe inflammation in the joint lining, is a progressive, chronic, autoimmune disorder with high morbidity and mortality rates. There are several mechanisms responsible for joint damage, but overproduction of TNF-α is a significant mechanism that results in excess swelling and pain. Drugs acting on TNF-α are known to significantly reduce the disease progression and improve the quality of life in many RA patients. Hence, inhibiting TNF-α is considered one of the most effective treatments for RA. Currently, there are only a few FDA-approved TNF-α inhibitors, which are mainly monoclonal antibodies, fusion proteins, or biosimilars with disadvantages such as poor stability, difficulty in route of administration (often given as injection or infusion), cost-prohibitive large-scale production, and increased side effects. There are just a handful of small compounds known to have TNF- inhibitory capabilities. Thus, there is a dire need for new drugs, especially small molecules in the market, such as TNF-α inhibitors. The conventional method of identifying TNF-α inhibitors is expensive, labor, and time intensive. Machine learning (ML) can be used to solve existing drug discovery and development problems. In this study, four classification algorithms-naïve Bayes (NB), random forest (RF), k-nearest neighbor (kNN), and support vector machine (SVM)-were used to train ML models for classifying TNF-α inhibitors based on three sets of features. The performance of the RF model was found to be best when using 1D, 2D, and fingerprints as features, with an accuracy of 87.96 and a sensitivity of 86.17. To our knowledge, this is the first ML model for TNF-α inhibitor prediction. The model is available at http://14.139.57.41/tnfipred/.

FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor.

Fibroblast growth factor receptors (FGFRs) are a family of cell surface receptors that bind to fibroblast growth factor (FGF) and mediate various cellular functions (translocating proteins, tissue repair, cell proliferation, development, and differentiation) through complex signaling pathways. The FGFR1 growth receptor is essential in the pathogenesis of numerous malignancies, including but not limited to breast cancer, bladder cancer, hepatocellular carcinoma (HCC), and cholangiocarcinoma. The higher levels of FGFR1 expression on the surface of cancer cells cause overly active signaling, which leads to rapid cell proliferation, resulting in a high spread of cancer cells. The kinases that FGFR1 activates migrate across the cell nucleus, activating genes and kinase proteins necessary for the growth and survival of cancerous cells. Therefore, FGFR1 targeting shows therapeutic promise in some diseases, including cancer. Inhibitors of FGFR1s are being developed and studied for their potential to block aberrant FGFR1 signaling and inhibit cancer growth. Since the discovery of new FGFR1 inhibitors in the laboratory is difficult, expensive, time-consuming, and labor-intensive, only a small number of FGFR1 inhibitors have been approved by the FDA for use in the treatment of cancer. To accelerate drug discovery by efficiently exploring the vast chemical space, and identifying potential candidates with higher accuracy and reduced cost, we developed artificial intelligence (AI)-based prediction models for FGFR1 inhibitors using a dataset of 2356 chemical compounds. Four machine learning (ML) algorithms (SVM, RF, k-NN, and ANN) were used to train different prediction models based on molecular descriptors (1D and 2D, with and without molecular fingerprints). Among all trained models, the random forest (RF)-based prediction model achieved the highest accuracy on the training (98.9%), test (89.8%), and external test (90.3%) datasets. The developed inhibitor prediction model (FGFR1Pred) provides a valuable tool for identifying potential FGFR1 inhibitors, expediting the drug discovery process and ultimately facilitating the development of new therapeutics. The model is made available at https://github.com/PGlab-NIPER/FGFR1Pred.git.

Carbamate as a potential anti-Alzheimer's pharmacophore: A review.

Alzheimer's disease (AD) is a progressive age-related neurodegenerative brain disorder, which leads to loss of memory and other cognitive dysfunction. The underlying mechanisms of AD pathogenesis are very complex and still not fully explored. Cholinergic neuronal loss, accumulation of amyloid plaque, metal ions dyshomeostasis, tau hyperphosphorylation, oxidative stress, neuroinflammation, and mitochondrial dysfunction are major hallmarks of AD. The current treatment options for AD are acetylcholinesterase inhibitors (donepezil, rivastigmine, and galantamine) and NMDA receptor antagonists (memantine). These FDA-approved drugs mainly provide symptomatic relief without addressing the pathological aspects of disease progression. So, there is an urgent need for novel drug development that not only addresses the basic mechanisms of the disease but also shows the neuroprotective property. Various research groups across the globe are working on the development of multifunctional agents for AD amelioration using different core scaffolds for their design, and carbamate is among them. Rivastigmine was the first carbamate drug investigated for AD management. The carbamate fragment, a core scaffold of rivastigmine, act as a potential inhibitor of acetylcholinesterase. In this review, we summarize the last 10 years of research conducted on the modification of carbamate with different substituents which primarily target ChE inhibition, reduce oxidative stress, and modulate Aß aggregation.

Machine learning-based modeling to predict inhibitors of acetylcholinesterase.

Acetylcholinesterase enzyme is responsible for the degradation of acetylcholine and is an important drug target for the treatment of Alzheimer's disease. When this enzyme is inhibited, more acetylcholine is available in the synaptic cleft for the use, which leads to enhanced memory and cognitive ability. The aim of the present work is to create machine learning models for distinguishing between AChE inhibitors and non-inhibitors using algorithms like support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The developed models were evaluated by 10-fold cross-validation and external dataset. Descriptor analysis was performed to identify most important features for the activity of molecules. Descriptors which were identified as important include maxssCH2, minHssNH, SaasC, minssCH2, bit 128 MACCS key, bit 104 MACCS key, bit 24 estate fingerprint and bit 18 estate fingerprints. The model developed using fingerprints based on random forest algorithm produced better results compared to other models. The overall accuracy of best model on test set was 85.38 percent. The developed model is available at http://14.139.57.41/achepredictor/ .

Further SAR studies on natural template based neuroprotective molecules for the treatment of Alzheimer's disease.

In our earlier paper, we described ferulic acid (FA) template based novel series of multifunctional cholinesterase (ChE) inhibitors for the management of AD. This report has further extended the structure-activity relationship (SAR) studies of this series of molecules in a calibrated manner to improve upon the ChEs inhibition and antioxidant property to identify the novel potent multifunctional molecules. To investigate the effect of replacement of phenylpiperazine ring with benzylpiperazine, increase in the linker length between FA and substituted phenyl ring, and replacement of indole moiety with tryptamine on this molecular template, three series of novel molecules were developed. All synthesized compounds were tested for their acetyl and butyryl cholinestrases (AChE and BChE) inhibitory properties. Enzyme inhibition and PAS binding studies identified compound 13b as a lead molecule with potent inhibitor property towards AChE/BChE (AChE IC50 = 0.96 ± 0.14 µM, BChE IC50 = 1.23 ± 0.23 µM) compared to earlier identified lead molecule EJMC-G (AChE IC50 = 5.74 ± 0.13 µM, BChE IC50 = 14.05 ± 0.10 µM, respectively). Molecular docking and dynamics studies revealed that 13b fits well into the active sites of AChE and BChE, forming stable and strong interactions with key residues Trp86, Ser125, Glu202, Trp 286, Phe295, Tyr 337 in AChE, and with Trp 82, Gly115, Tyr128, and Ser287 in BChE. The compound, 13b was found to be three times more potent antioxidant in a DPPH assay (IC50 = 20.25 ± 0.26 µM) over the earlier identified EJMC-B (IC50 = 61.98 ± 0.30 µM) and it also was able to chelate iron. Co-treatment of 13b with H2O2, significantly attenuated and reversed H2O2-induced toxicity in the SH-SY5Y cells. The parallel artificial membrane permeability assay-blood brain barrier (PAMPA-BBB) revealed that 13b could cross BBB efficiently. Finally, the in-vivo efficacy of 13b at dose of 10 mg/kg in scopolamine AD model has been demonstrated. The present study strongly suggests that the naturally inspired multifunctional molecule 13b may behave as a potential novel therapeutic agent for AD management.

Structure-Based Virtual Screening to Discover Potential Lead Molecules for the SARS-CoV-2 Main Protease.

The COVID-19 disease is caused by a new strain of the coronavirus family (SARS-CoV-2), and it has affected at present millions of people all over the world. The indispensable role of the main protease (Mpro) in viral replication and gene expression makes this enzyme an attractive drug target. Therefore, inhibition of SARS-CoV-2 Mpro as a proposition to halt virus ingression is being pursued by scientists globally. Here we carried out a study with two objectives: the first being to perform comparative protein sequence and 3D structural analysis to understand the effect of 12 point mutations on the active site. Among these, two mutations, viz., Ser46 and Phe134, were found to cause a significant change at the active sites of SARS-CoV-2. The Ser46 mutation present at the entrance of the S5 subpocket of SARS-CoV-2 increases the contribution of other two hydrophilic residues, while the Phe134 mutation, present in the catalytic cysteine loop, can cause an increase in catalytic efficiency of Mpro by facilitating fast proton transfer from the Cys145 to His41 residue. It was observed that active site remained conserved among Mpro of both SARS-CoVs, except at the entrance of the S5 subpocket, suggesting sustenance of substrate specificity. The second objective was to screen the inhibitory effects of three different data sets (natural products, coronaviruses main protease inhibitors, and FDA-approved drugs) using a structure-based virtual screening approach. A total of 73 hits had a combo score >2.0. Eight different structural scaffold classes were identified, such as one/two tetrahydropyran ring(s), dipeptide/tripeptide/oligopeptide, large (approximately 20 atoms) cyclic peptide, and miscellaneous. The screened hits showed key interactions with subpockets of the active site. Further, molecular dynamics studies of selected screened compounds confirmed their perfect fitting into the subpockets of the active site. This study suggests promising structures that can fit into the SARS-CoV-2 Mpro active site and also offers direction for further lead optimization and rational drug design.

Characterization of forced degradation products of canagliflozine by liquid chromatography/quadrupole time-of-flight tandem mass spectrometry and in silico toxicity predictions.

RATIONALE: Forced degradation studies are useful for better understanding of the stability of active pharmaceutical ingredients and drugs and to generate information about drug degradation pathways and formation of degradation products (DPs). Identification of DPs plays a vital role in establishing the safety and therapeutic benefit of a drug. METHODS: Canagliflozin (CAN) was subjected to different stress conditions as per International Conference on Harmonization guidelines (Q1A R2). All the DPs and the drug were well separated on an Aquity CSH C18 (100 × 2.1 mm, 1.7 µm) column using acetonitrile-methanol (70:30, v/v) and formic acid in gradient mode. The same UPLC method was employed for LC/HRMS for the characterization of DPs. In addition, in silico toxicity was predicted for all the DPs by using TOPKAT and DEREK software tools. RESULTS: CAN was found to degrade under oxidative stress condition and formed DP1 and DP2. This is a typical case of degradation where co-solvents acetonitrile-water (50:50, v/v) and methanol-water (50:50, v/v) react with CAN under acid hydrolytic conditions leading to the formation of pseudo-DPs, DP3 and DP4, respectively. Among these, DP2 and DP3 showed ocular irritancy whereas DP1 showed skin sensitization. CONCLUSIONS: The drug was labile under oxidative stress condition. CAN reacted with co-solvent under acid hydrolytic conditions and gave pseudo-DPs. All the DPs were separated using UPLC and characterized by LC/QTOF/MS/MS. Toxicity of DPs was evaluated using TOPKAT and DEREK software tools.

Identification and characterization of vilazodone metabolites in rats and microsomes by ultrahigh-performance liquid chromatography/quadrupole time-of-flight tandem mass spectrometry.

RATIONALE: Vilazodone is a selective serotonin reuptake inhibitor (SSRI) used for the treatment of major depressive disorder (MDD). An extensive literature search found few reports on the in vivo and in vitro metabolism of vilazodone. Therefore, we report a comprehensive in vivo and in vitro metabolic identification and structural characterization of vilazodone using ultrahigh-performance liquid chromatography/quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF/MS/MS) and in silico toxicity study of the metabolites. METHODS: To identify in vivo metabolites of vilazodone, blood, urine and faeces samples were collected at different time intervals starting from 0 h to 48 h after oral administration of vilazodone to Sprague-Dawley rats. The in vitro metabolism study was conducted with human liver microsomes (HLM) and rat liver microsomes (RLM). The samples were prepared using an optimized sample preparation approach involving protein precipitation followed by solid-phase extraction. The metabolites have been identified and characterized by using LC/ESI-MS/MS. RESULTS: A total of 12 metabolites (M1-M12) were identified in in vivo and in vitro matrices and characterized by LC/ESI-MS/MS. The majority of the metabolites were observed in urine, while a few metabolites were present in faeces and plasma. Two metabolites were observed in the in vitro study. A semi-quantitative study based on percentage counts shows that metabolites M11, M6 and M8 were observed in higher amounts in urine, faeces and plasma, respectively. CONCLUSIONS: The structures of all the 12 metabolites were elucidated by using LC/ESI-MS/MS. The study suggests that vilazodone was metabolized via hydroxylation, dihydroxylation, glucuronidation, oxidative deamination, dealkylation, dehydrogenation and dioxidation. All the metabolites were screened for toxicity using an in silico tool.

Selective Fusion of Heterogeneous Classifiers for Predicting Substrates of Membrane Transporters.

Membrane transporters play a crucial role in determining fate of administered drugs in a biological system. Early identification of plausible transporters for a drug molecule can provide insights into its therapeutic, pharmacokinetic, and toxicological profiles. In the present study, predictive models for classifying small molecules into substrates and nonsubstrates of various pharmaceutically important membrane transporters were developed using quantitative structure-activity relationship (QSAR) and proteochemometric (PCM) approaches. For this purpose, 4575 substrate interactions for these transporters were collected from the Metabolism and Transport Database (Metrabase) and the literature. The transporters selected for this study include (i) six efflux transporters, viz., breast cancer resistance protein (BCRP/ABCG2), P-glycoprotein (P-gp/MDR1), and multidrug resistance proteins (MRP1, MRP2, MRP3, and MRP4), and (ii) seven influx transporters, viz., organic cation transporter (OCT1/SO22A1), peptide transporter (PEPT1/SO15A1), apical sodium-bile acid transporter (ASBT/NTCP2), and organic anion transporting peptides (OATP1A2/SO1A2, OATP1B/SO1B1, OATP1B3/SO1B3, and OATP2B1/SO2B1). Various types of descriptors and machine learning methods (classifiers) were evaluated for the development of robust predictive models. Additionally, ensemble models were developed by bagging of homogeneous classifiers and selective fusion of heterogeneous classifiers. It was observed that the latter approach improves the accuracy of substrate/nonsubstrate prediction for transporters (average correct classification rate of more than 0.80 for external validation). Moreover, structural fragments important in determining the substrate specificity across the various transporters were identified. To demonstrate these fragments on the query molecule, contour maps were generated. The prediction efficacy of the developed models was illustrated by a good correlation between the reported logBB value of a molecule and its predicted substrate propensity for blood-brain barrier transporters. Conclusively, this comprehensive modeling analysis can be efficiently employed for the prediction of membrane transporters of a drug, thereby providing insights into its pharmacological profile.

The role of multidrug resistance protein (MRP-1) as an active efflux transporter on blood-brain barrier (BBB) permeability.

Drugs acting on central nervous system (CNS) may take longer duration to reach the market as these compounds have a higher attrition rate in clinical trials due to the complexity of the brain, side effects, and poor blood-brain barrier (BBB) permeability compared to non-CNS-acting compounds. The roles of active efflux transporters with BBB are still unclear. The aim of the present work was to develop a predictive model for BBB permeability that includes the MRP-1 transporter, which is considered as an active efflux transporter. A support vector machine model was developed for the classification of MRP-1 substrates and non-substrates, which was validated with an external data set and Y-randomization method. An artificial neural network model has been developed to evaluate the role of MRP-1 on BBB permeation. A total of nine descriptors were selected, which included molecular weight, topological polar surface area, ClogP, number of hydrogen bond donors, number of hydrogen bond acceptors, number of rotatable bonds, P-gp, BCRP, and MRP-1 substrate probabilities for model development. We identified 5 molecules that fulfilled all criteria required for passive permeation of BBB, but they all have a low logBB value, which suggested that the molecules were effluxed by the MRP-1 transporter.

Automated statistical experimental design approach for rapid separation of coenzyme Q10 and identification of its biotechnological process related impurities using UHPLC and UHPLC-APCI-MS.

A novel ultra high performance liquid chromatography method development strategy was ameliorated by applying quality by design approach. The developed systematic approach was divided into five steps (i) Analytical Target Profile, (ii) Critical Quality Attributes, (iii) Risk Assessments of Critical parameters using design of experiments (screening and optimization phases), (iv) Generation of design space, and (v) Process Capability Analysis (Cp) for robustness study using Monte Carlo simulation. The complete quality-by-design-based method development was made automated and expedited by employing sub-2 µm particles column with an ultra high performance liquid chromatography system. Successful chromatographic separation of the Coenzyme Q10 from its biotechnological process related impurities was achieved on a Waters Acquity phenyl hexyl (100 mm × 2.1 mm, 1.7 µm) column with gradient elution of 10 mM ammonium acetate buffer (pH 4.0) and a mixture of acetonitrile/2-propanol (1:1) as the mobile phase. Through this study, fast and organized method development workflow was developed and robustness of the method was also demonstrated. The method was validated for specificity, linearity, accuracy, precision, and robustness in compliance to the International Conference on Harmonization, Q2 (R1) guidelines. The impurities were identified by atmospheric pressure chemical ionization-mass spectrometry technique. Further, the in silico toxicity of impurities was analyzed using TOPKAT and DEREK software.

Molecular mechanism of poly(vinyl alcohol) mediated prevention of aggregation and stabilization of insulin in nanoparticles.

It is a challenge to formulate polymer based nanoparticles of therapeutic proteins as excipients and process conditions affect stability and structural integrity of the protein. Hence, understanding the protein stability and complex aggregation phenomena is an important area of research in therapeutic protein delivery. Herein we investigated the comparative role of three kinds of surfactant systems (Tween 20:Tween 80), small molecular weight poly(vinyl alcohol) (SMW-PVA), and high molecular weight PVA (HMW-PVA) in prevention of aggregation and stabilization of hexameric insulin in poly(lactide-co-glycolide) (PLGA) based nanoparticle formulation. The nanoparticles were prepared using solid-in-oil-in-water (S/O/W) emulsification method with one of the said surfactant system in inner aqueous phase. The thermal unfolding analysis of released insulin using circular dichroism (CD) indicated thermal stability of the hexameric form. Insulin aggregation monitored by differential scanning calorimetry (DSC) suggested the importance of nuclei formation for aggregation and its prevention by HMW-PVA. Additional guanidinium hydrochloride based equilibrium unfolding and in silico (molecular docking) studies suggested maximum stability of released insulin from formulation containing HMW-PVA (F3). Furthermore, in vivo studies of insulin loaded nanoparticle formulation (F3) in diabetic rats showed its bioactivity. In conclusion, our studies highlight the importance of C-terminal residues of insulin in structural integrity and suggest that the released insulin from formulation containing HMW-PVA in inner aqueous phase was conformationally and thermodynamically stable and bioactive in vivo.

Role of breast cancer resistance protein (BCRP) as active efflux transporter on blood-brain barrier (BBB) permeability.

Nowadays most of the CNS acting therapeutic molecules are failing in clinical trials due to efflux transporters at the blood brain barrier (BBB) which imparts resistance and poor ADMET properties of these molecules. CNS acting drug molecules interact with the BBB prior to their target site, so there is a need to develop predictive models for BBB permeability which can be used in the initial phases of drug discovery process. Most of the drug molecules are transported to the brain via passive diffusion which is explored extensively; on the other hand, the role of active efflux transporters in BBB permeability is unclear. Our aim is to develop predictive models for BBB permeability that include active efflux transporters. An in silico model has been developed to assess the role of BCRP on BBB permeation. Eight descriptors were selected, which also include BCRP substrate probabilities used for model development and show a relationship between BCRP and logBB. From our analysis, it was found that 11 molecules satisfied all criteria required for BBB permeation but have low logBB values. These 11 molecules are predicted as BCRP substrates from the model developed, suggesting that the molecules are effluxed by the BCRP transporter. This predictive ability was further validated by docking of these 11 molecules into BCRP protein. This study provides a new mechanistic insight into correlation of low logBB values and efflux mechanism of BCRP in BBB.

CRAFT: a web-integrated cavity prediction tool based on flow transfer algorithm.

Numerous computational methods, including evolutionary-based, energy-based, and geometrical-based methods, are utilized to identify cavities inside proteins. Cavity information aids protein function annotation, drug design, poly-pharmacology, and allosteric site investigation. This article introduces "flow transfer algorithm" for rapid and effective identification of diverse protein cavities through multidimensional cavity scan. Initially, it identifies delimiter and susceptible tetrahedra to establish boundary regions and provide seed tetrahedra. Seed tetrahedron faces are precisely scanned using the maximum circle radius to transfer seed flow to neighboring tetrahedra. Seed flow continues until terminated by boundaries or forbidden faces, where a face is forbidden if the estimated maximum circle radius is less or equal to the user-defined maximum circle radius. After a seed scanning, tetrahedra involved in the flow are clustered to locate the cavity. The CRAFT web interface integrates this algorithm for protein cavity identification with enhanced user control. It supports proteins with cofactors, hydrogens, and ligands and provides comprehensive features such as 3D visualization, cavity physicochemical properties, percentage contribution graphs, and highlighted residues for each cavity. CRAFT can be accessed through its web interface at http://pitools.niper.ac.in/CRAFT , complemented by the command version available at https://github.com/PGlab-NIPER/CRAFT/ .Scientific contribution: Flow transfer algorithm is a novel geometric approach for accurate and reliable prediction of diverse protein cavities. This algorithm employs a distinct concept involving maximum circle radius within the 3D Delaunay triangulation to address diverse van der Waals radii while existing methods overlook atom specific van der Waals radii or rely on complex weighted geometric techniques.

Structural-functional analysis of drug target aspartate semialdehyde dehydrogenase.

Aspartate ß-semialdehyde dehydrogenase (ASADH) is a key enzyme in the biosynthesis of essential amino acids in microorganisms and some plants. Inhibition of ASADHs can be a potential drug target for developing novel antimicrobial and herbicidal compounds. This review covers up-to-date information about sequence diversity, ligand/inhibitor-bound 3D structures, potential inhibitors, and key pharmacophoric features of ASADH useful in designing novel and target-specific inhibitors of ASADH. Most reported ASADH inhibitors have two highly electronegative functional groups that interact with two key arginyl residues present in the active site of ASADHs. The structural information, active site binding modes, and key interactions between the enzyme and inhibitors serve as the basis for designing new and potent inhibitors against the ASADH family.

High-throughput virtual screening of phloroglucinol derivatives against HIV-reverse transcriptase.

Therapeutic agents exert their pharmacological and adverse effects by interacting with molecular targets. Even if drug molecules are intended to interact with specific targets in a desirable manner, they are often found to bind to other targets. Nowadays, research is focused on a single molecule that simultaneously targets multiple disease-causing proteins. Therefore, off-target identification of existing chemical space can be a valuable tool to find safe and effective multi-targeted therapeutic agents at a significantly lower cost to patients. Phloroglucinols represent a class of compounds, which exhibits a diverse range of biological activities, such as anti-HIV, antimalarial, antileishmanial, antituberculosis, antibacterial, and antifungal. The aim of the current study is to explore untapped potential of various series of phloroglucinols against HIV reverse transcriptase (HIV-RTase). A series of filtering parameters was applied in search of viable phloroglucinol derivatives against HIV-RTase. A library of phloroglucinol derivatives was screened based on their toxicity potential followed by predicted ADME parameters. The filtered compounds were then carried forward for docking analysis against HIV-RTase. A set of 37 phloroglucinol compounds with diverse pharmacological profile was found to have good binding affinity towards HIV-RTase. These molecules formed hydrogen bonds with Lys101, Lys103, Val106, and Leu234 residues and π­π stacking interaction with Tyr318 residue of the protein. Here, we propose potential phloroglucinol derivatives with different known biological activity that can be repurposed as potential hits against HIV.

Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images.

BACKGROUND: Gastrointestinal (GI) infections are quite common today around the world. Colonoscopy or wireless capsule endoscopy (WCE) are noninvasive methods for examining the whole GI tract for abnormalities. Nevertheless, it requires a great deal of time and effort for doctors to visualize a large number of images, and diagnosis is prone to human error. As a result, developing automated artificial intelligence (AI) based GI disease diagnosis methods is a crucial and emerging research area. AI-based prediction models may lead to improvements in the early diagnosis of gastrointestinal disorders, assessing severity, and healthcare systems for the benefit of patients as well as clinicians. The focus of this research is on the early diagnosis of gastrointestinal diseases using a convolution neural network (CNN) to enhance diagnosis accuracy. METHODS: Various CNN models (baseline model and using transfer learning (VGG16, InceptionV3, and ResNet50)) were trained on a benchmark image dataset, KVASIR, containing images from inside the GI tract using n-fold cross-validation. The dataset comprises images of three disease states-polyps, ulcerative colitis, and esophagitis-as well as images of the healthy colon. Data augmentation strategies together with statistical measures were used to improve and evaluate the model's performance. Additionally, the test set comprising 1200 images was used to evaluate the model's accuracy and robustness. RESULTS: The CNN model using the weights of the ResNet50 pre-trained model achieved the highest average accuracy of approximately 99.80% on the training set (100% precision and approximately 99% recall) and accuracies of 99.50% and 99.16% on the validation and additional test set, respectively, while diagnosing GI diseases. When compared to other existing systems, the proposed ResNet50 model outperforms them all. CONCLUSION: The findings of this study indicate that AI-based prediction models using CNNs, specifically ResNet50, can improve diagnostic accuracy for detecting gastrointestinal polyps, ulcerative colitis, and esophagitis. The prediction model is available at https://github.com/anjus02/GI-disease-classification.git.