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Showing 441 to 444 of 5186 entries
Published on December 23, 2022
miDruglikeness: Subdivisional Drug-Likeness Prediction Models Using Active Ensemble Learning Strategies.
Abstract: The drug development pipeline involves several stages including in vitro assays, in vivo assays, and clinical trials. For candidate selection, it is important to consider that a compound will successfully pass through these stages. Using graph neural networks, we developed three subdivisional models to individually predict the capacity of a compound to enter in vivo testing, clinical trials, and market approval stages. Furthermore, we proposed a strategy combing both active learning and ensemble learning to improve the quality of the models. The models achieved satisfactory performance in the internal test datasets and four self-collected external test datasets. We also employed the models as a general index to make an evaluation on a widely known benchmark dataset DEKOIS 2.0, and surprisingly found a powerful ability on virtual screening tasks. Our model system (termed as miDruglikeness) provides a comprehensive drug-likeness prediction tool for drug discovery and development.
Published on December 23, 2022
A Guide to In Silico Drug Design.
Abstract: The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.
Published on December 22, 2022
Structure-Based Design of a Dual-Targeted Covalent Inhibitor Against Papain-like and Main Proteases of SARS-CoV-2.
Abstract: The two proteases, PL(pro) and M(pro), of SARS-CoV-2 are essential for replication of the virus. Using a structure-based co-pharmacophore screening approach, we developed a novel dual-targeted inhibitor that is equally potent in inhibiting PL(pro) and M(pro) of SARS-CoV-2. The inhibitor contains a novel warhead, which can form a covalent bond with the catalytic cysteine residue of either enzyme. The maximum rate of the covalent inactivation is comparable to that of the most potent inhibitors reported for the viral proteases and covalent inhibitor drugs currently in clinical use. The covalent inhibition appears to be very specific for the viral proteases. The inhibitor has a potent antiviral activity against SARS-CoV-2 and is also well tolerated by mice and rats in toxicity studies. These results suggest that the inhibitor is a promising lead for development of drugs for treatment of COVID-19.
Published on December 22, 2022
CD4+ T Cell Regulatory Network Underlies the Decrease in Th1 and the Increase in Anergic and Th17 Subsets in Severe COVID-19.
Abstract: In this model we use a dynamic and multistable Boolean regulatory network to provide a mechanistic explanation of the lymphopenia and dysregulation of CD4+ T cell subsets in COVID-19 and provide therapeutic targets. Using a previous model, the cytokine micro-environments found in mild, moderate, and severe COVID-19 with and without TGF-beta and IL-10 was we simulated. It shows that as the severity of the disease increases, the number of antiviral Th1 cells decreases, while the the number of Th1-like regulatory and exhausted cells and the proportion between Th1 and Th1R cells increases. The addition of the regulatory cytokines TFG-beta and IL-10 makes the Th1 attractor unstable and favors the Th17 and regulatory subsets. This is associated with the contradictory signals in the micro-environment that activate SOCS proteins that block the signaling pathways. Furthermore, it determined four possible therapeutic targets that increase the Th1 compartment in severe COVID-19: the activation of the IFN-gamma pathway, or the inhibition of TGF-beta or IL-10 pathways or SOCS1 protein; from these, inhibiting SOCS1 has the lowest number of predicted collateral effects. Finally, a tool is provided that allows simulations of specific cytokine environments and predictions of CD4 T cell subsets and possible interventions, as well as associated secondary effects.
Published on December 21, 2022
Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer.
Abstract: Non-small cell lung cancer (NSCLC) remains the leading cause of mortality and morbidity worldwide accounting about 85% of total lung cancer cases. The receptor REarranged during Transfection (RET) plays an important role by ligand independent activation of kinase domain resulting in carcinogenesis. Presently, the treatment for RET driven NSCLC is limited to multiple kinase inhibitors. This situation necessitates the discovery of novel and potent RET specific inhibitors. Thus, we employed high throughput screening strategy to repurpose FDA approved compounds from DrugBank comprising of 2509 molecules. It is worth noting that the initial screening is accomplished with the aid of in-house machine learning model built using IC(50) values corresponding to 2854 compounds obtained from BindingDB repository. A total of 497 compounds (19%) were predicted as actives by our generated model. Subsequent in silico validation process such as molecular docking, MMGBSA and density function theory analysis resulted in identification of two lead compounds named DB09313 and DB00471. The simulation study highlights the potency of DB00471 (Montelukast) as potential RET inhibitor among the investigated compounds. In the end, the half-minimal inhibitory activity of montelukast was also predicted against RET protein expressing LC-2/ad cell lines demonstrated significant anticancer activity. Collective analysis from our study highlights that montelukast could be a promising candidate for the management of RET specific NSCLC.
Published on December 20, 2022
Fluxomics reveals cellular and molecular basis of increased renal ammoniagenesis.
Abstract: The kidney plays a critical role in excreting ammonia during metabolic acidosis and liver failure. The mechanisms behind this process have been poorly explored. The present study combines results of in vivo experiments of increased total ammoniagenesis with systems biology modeling, in which eight rats were fed an amino acid-rich diet (HD group) and eight a normal chow diet (AL group). We developed a method based on elementary mode analysis to study changes in amino acid flux occurring across the kidney in increased ammoniagenesis. Elementary modes represent minimal feasible metabolic paths in steady state. The model was used to predict amino acid fluxes in healthy and pre-hyperammonemic conditions, which were compared to experimental fluxes in rats. First, we found that total renal ammoniagenesis increased from 264 +/- 68 to 612 +/- 87 nmol (100 g body weight)(-1) min(-1) in the HD group (P = 0.021) and a concomitated upregulation of NKCC2 ammonia and other transporters in the kidney. In the kidney metabolic model, the best predictions were obtained with ammonia transport as an objective. Other objectives resulting in a fair correlation with the measured fluxes (correlation coefficient >0.5) were growth, protein uptake, urea excretion, and lysine and phenylalanine transport. These predictions were improved when specific gene expression data were considered in HD conditions, suggesting a role for the mitochondrial glycine pathway. Further studies are needed to determine if regulation through the mitochondrial glycine pathway and ammonia transporters can be modulated and how to use the kidney as a therapeutic target in hyperammonemia.
Published on December 20, 2022
Unsupervised Machine Learning Organization of the Functional Dark Proteome of Gram-Negative "Superbugs": Six Protein Clusters Amenable for Distinct Scientific Applications.
Abstract: Uncharacterized proteins have been underutilized as targets for the development of novel therapeutics for difficult-to-treat bacterial infections. To facilitate the exploration of these proteins, 2819 predicted, uncharacterized proteins (19.1% of the total) from reference strains of multidrug Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa species were organized using an unsupervised k-means machine learning algorithm. Classification using normalized values for protein length, pI, hydrophobicity, degree of conservation, structural disorder, and %AT of the coding gene rendered six natural clusters. Cluster proteins showed different trends regarding operon membership, expression, presence of unknown function domains, and interactomic relevance. Clusters 2, 4, and 5 were enriched with highly disordered proteins, nonworkable membrane proteins, and likely spurious proteins, respectively. Clusters 1, 3, and 6 showed closer distances to known antigens, antibiotic targets, and virulence factors. Up to 21.8% of proteins in these clusters were structurally covered by modeling, which allowed assessment of druggability and discontinuous B-cell epitopes. Five proteins (4 in Cluster 1) were potential druggable targets for antibiotherapy. Eighteen proteins (11 in Cluster 6) were strong B-cell and T-cell immunogen candidates for vaccine development. Conclusively, we provide a feature-based schema to fractionate the functional dark proteome of critical pathogens for fundamental and biomedical purposes.
Published on December 20, 2022
Effect of phosphorylation of protamine-like cationic peptide on the binding affinity to DNA.
Abstract: Protamines are more arginine-rich and more basic than histones and are responsible for providing a highly compacted shape to the sperm heads in the testis. Phosphorylation and dephosphorylation are two events that occur in the late phase of spermatogenesis before the maturation of sperms. In this work, we have studied the effect of phosphorylation of protamine-like cationic peptides using all-atom molecular dynamics simulations. Through thermodynamic analyses, we found that phosphorylation reduces the binding efficiency of such cationic peptides on DNA duplexes. Peptide phosphorylation leads to a less efficient DNA condensation, due to a competition between DNA-peptide and peptide-peptide interactions. We hypothesize that the decrease of peptide bonds between DNA together with peptide self-assembly might allow an optimal re-organization of chromatin and an efficient condensation through subsequent peptide dephosphorylation. Based on the globular and compact conformations of phosphorylated peptides mediated by arginine-phosphoserine H-bonding, we furthermore postulate that phosphorylated protamines could more easily intrude into chromatin and participate to histone release through disruption of histone-histone and histone-DNA binding during spermatogenesis.
Published on December 19, 2022
Therapeutic Target Identification and Inhibitor Screening against Riboflavin Synthase of Colorectal Cancer Associated Fusobacterium nucleatum.
Abstract: Colorectal cancer (CRC) ranks third among all cancers in terms of prevalence. There is growing evidence that gut microbiota has a role in the development of colorectal cancer. Fusobacterium nucleatum is overrepresented in the gastrointestinal tract and tumor microenvironment of patients with CRC. This suggests the role of F. nucleatum as a potential risk factor in the development of CRC. Hence, we aimed to explore whole genomes of F. nucleatum strains related to CRC to predict potential therapeutic markers through a pan-genome integrated subtractive genomics approach. In the current study, we identified 538 proteins as essential for F. nucleatum survival, 209 non-homologous to a human host, and 12 as drug targets. Eventually, riboflavin synthase (RiS) was selected as a therapeutic target for further processing. Three different inhibitor libraries of lead-like natural products, i.e., cyanobactins (n = 237), streptomycins (n = 607), and marine bacterial secondary metabolites (n = 1226) were screened against it. After the structure-based study, three compounds, i.e., CMNPD3609 (-7.63) > Malyngamide V (-7.03) > ZINC06804365 (-7.01) were prioritized as potential inhibitors of F. nucleatum. Additionally, the stability and flexibility of these compounds bound to RiS were determined via a molecular dynamics simulation of 50 ns. Results revealed the stability of these compounds within the binding pocket, after 5 ns. ADMET profiling showed compounds as drug-like, non-permeable to the blood brain barrier, non-toxic, and HIA permeable. Pan-genomics mediated drug target identification and the virtual screening of inhibitors is the preliminary step towards inhibition of this pathogenic oncobacterium and we suggest mouse model experiments to validate our findings.
Published on December 19, 2022
Machine learning prediction of side effects for drugs in clinical trials.
Abstract: Early and accurate detection of side effects is critical for the clinical success of drugs under development. Here, we aim to predict unknown side effects for drugs with a small number of side effects identified in randomized controlled clinical trials. Our machine learning framework, the geometric self-expressive model (GSEM), learns globally optimal self-representations for drugs and side effects from pharmacological graph networks. We show the usefulness of the GSEM on 505 therapeutically diverse drugs and 904 side effects from multiple human physiological systems. Here, we also show a data integration strategy that could be adopted to improve the ability of side effect prediction models to identify unknown side effects that might only appear after the drug enters the market.
Showing 441 to 444 of 5186 entries