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Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful examples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug-target, target-disease, and ultimately drug-disease associations.

The dense array of N-linked glycans on the HIV-1 envelope glycoprotein (Env), known as the “glycan shield,” is a key determinant of immunogenicity, yet intrinsic heterogeneity confounds typical structure–function analysis. Here, we present an integrated approach of single-particle electron cryomicroscopy (cryo-EM), computational modeling, and site-specific mass spectrometry (MS) to probe glycan shield structure and behavior at multiple levels. We found that dynamics lead to an extensive network of interglycan interactions that drive the formation of higher-order structure within the glycan shield. This structure defines diffuse boundaries between buried and exposed protein surface and creates a mapping of potentially immunogenic sites on Env. Analysis of Env expressed in different cell lines revealed how cryo-EM can detect subtle changes in glycan occupancy, composition, and dynamics that impact glycan shield structure and epitope accessibility. Importantly, this identified unforeseen changes in the glycan shield of Env obtained from expression in the same cell line used for vaccine production. Finally, by capturing the enzymatic deglycosylation of Env in a time-resolved manner, we found that highly connected glycan clusters are resistant to digestion and help stabilize the prefusion trimer, suggesting the glycan shield may function beyond immune evasion.

The mechanisms involved in the formation/dissociation of methane hydrate confined at the nanometer scale are unraveled using advanced molecular modeling techniques combined with a mesoscale thermodynamic approach. Using atom-scale simulations probing coexistence upon confinement and free energy calculations, phase stability of confined methane hydrate is shown to be restricted to a narrower temperature and pressure domain than its bulk counterpart. The melting point depression at a given pressure, which is consistent with available experimental data, is shown to be quantitatively described using the Gibbs–Thomson formalism if used with accurate estimates for the pore/liquid and pore/hydrate interfacial tensions. The metastability barrier upon hydrate formation and dissociation is found to decrease upon confinement, therefore providing a molecular-scale picture for the faster kinetics observed in experiments on confined gas hydrates. By considering different formation mechanisms—bulk homogeneous nucleation, external surface nucleation, and confined nucleation within the porosity—we identify a cross-over in the nucleation process; the critical nucleus formed in the pore corresponds either to a hemispherical cap or to a bridge nucleus depending on temperature, contact angle, and pore size. Using the classical nucleation theory, for both mechanisms, the typical induction time is shown to scale with the pore volume to surface ratio and hence the pore size. These findings for the critical nucleus and nucleation rate associated with such complex transitions provide a means to rationalize and predict methane hydrate formation in any porous media from simple thermodynamic data.

Dynamic communities in proteins comprise the cohesive structural units that individually exhibit rigid body motions. These can correspond to structural domains, but are usually smaller parts that move with respect to one another in a protein's internal motions, key to its functional dynamics. Previous studies emphasized their importance to understand the nature of ligand-induced allosteric regulation. These studies reported that mutations to key community residues can hinder transmission of allosteric signals among the communities. Usually molecular dynamic (MD) simulations (~ 100 ns or longer) have been used to identify the communities-a demanding task for larger proteins. In the present study, we propose that dynamic communities obtained from MD simulations can also be obtained alternatively with simpler models-the elastic network models (ENMs). To verify this premise, we compare the specific communities obtained from MD and ENMs for 44 proteins. We evaluate the correspondence in communities from the two methods and compute the extent of agreement in the dynamic cross-correlation data used for community detection. Our study reveals a strong correspondence between the communities from MD and ENM and also good agreement for the residue cross-correlations. Importantly, we observe that the dynamic communities from MD can be closely reproduced with ENMs. With ENMs, we also compare the community structures of stable and unstable mutant forms of T4 Lysozyme with its wild-type. We find that communities for unstable mutants show substantially poorer agreement with the wild-type communities than do stable mutants, suggesting such ENM-based community structures can serve as a means to rapidly identify deleterious mutants.

Dengue virus Type 2 (DENV-2) is predominant serotype causing major dengue epidemics. There are a number of studies carried out to find its effective antiviral, however to date, there is still no molecule either from peptide or small molecules released as a drug. The present study aims to identify small molecules inhibitor from National Cancer Institute database through virtual screening. One of the hits, D0713 (IC50 = 62 μM) bearing thioguanine scaffold was derivatised into 21 compounds and evaluated for DENV-2 NS2B/NS3 protease inhibitory activity. Compounds 18 and 21 demonstrated the most potent activity with IC50 of 0.38 μM and 16 μM, respectively. Molecular dynamics and MM/PBSA free energy of binding calculation were conducted to study the interaction mechanism of these compounds with the protease. The free energy of binding of 18 calculated by MM/PBSA is -16.10 kcal/mol compared to the known inhibitor, panduratin A (-11.27 kcal/mol), which corroborates well with the experimental observation. Results from molecular dynamics simulations also showed that both 18 and 21 bind in the active site and stabilised by the formation of hydrogen bonds with Asn174.

Natural products have a significant role in drug discovery. Natural products have distinctive chemical structures that have contributed to identifying and developing drugs for different therapeutic areas. Moreover, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. Natural products such as peptides and macrocycles, and other compounds with unique features represent attractive sources to address complex diseases. Computational approaches that use chemoinformatics and molecular modeling methods contribute to speed up natural product-based drug discovery. Several research groups have recently used computational methodologies to organize data, interpret results, generate and test hypotheses, filter large chemical databases before the experimental screening, and design experiments. This review discusses a broad range of chemoinformatics applications to support natural product-based drug discovery. We emphasize profiling natural product data sets in terms of diversity; complexity; acid/base; absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties; and fragment analysis. Novel techniques for the visual representation of the chemical space are also discussed.

Ubiquitin-specific peptidase 7 (USP7) is a deubiquitinating enzyme that mediates the stability and activity of numerous proteins. At basal expression levels, USP7 stabilizes p53 protein, even in the presence of excess MDM2. However, its overexpression leads to the deubiquitination of MDM2 at a rate faster than p53, leading to p53 degradation and pro-tumorigenic roles. Consequently, it is an attractive target for anticancer drug discovery via the modulation of its allosteric site from which the protein is activated. In this study, molecular modeling techniques and cheminformatics approaches were employed to unravel the potential of eighty compounds to serve as its allosteric site modulators. The compounds were initially subjected to virtual screening. Subsequently, the binding free energies of the top four compounds with the highest binding affinities were calculated, and their drug-likeness, and pharmacokinetic and toxicity profiles were evaluated. Ultimately, the complexes of the protein and hit compounds were subjected to a 100 nanoseconds (ns) molecular dynamics simulation. The results of the study revealed eight compounds from the compound library with docking scores ranging from − 7.491 to -11.43 kcal/mol, compared to P217564, which exhibited a docking score of -5.671 kcal/mol. The top four compounds with the highest affinities possessed drug-like properties, and good pharmacokinetic and toxicity profiles, and their predicted inhibitory potentials showed they will be effective at minimal concentration. Also, molecular dynamics simulation confirmed the stability of the protein-ligand complexes. Conclusively, the compounds identified in this study are worthy of further evaluation for the development of allosteric site modulators of USP7.

Software and Programming Tools in Pharmaceutical Research is a detailed primer on the use for computer programs in the design and development of new drugs. Chapters offer information about different programs and computational techniques in pharmacology. The book will help readers to harness computer technologies in pharmaceutical investigations. Readers will also appreciate the pivotal role that software applications and programming tools play in revolutionizing the pharmaceutical industry. The book includes nine structured chapters, each addressing a critical aspect of pharmaceutical research and software utilization. From an introduction to pharmaceutical informatics and computational chemistry to advanced topics like molecular modeling, data mining, and high-throughput screening, this book covers a wide range of topics. Key Features: · Practical Insights: Presents practical knowledge on how to effectively utilize software tools in pharmaceutical research. · Interdisciplinary Approach: Bridges the gap between pharmaceutical science and computer science · Cutting-Edge Topics: Covers the latest advancements in computational drug development, including data analysis and visualization techniques, drug repurposing, pharmacokinetic modelling and screening. · Recommendations for Tools: Includes informative tables for software tools · Referenced content: Includes scientific references for advanced readers The book is an ideal primer for students and educators in pharmaceutical science and computational biology, providing a comprehensive foundation for this rapidly evolving field. It is also an essential resource for pharmaceutical researchers, scientists, and professionals looking to enhance their understanding of software tools and programming in drug development. Readership Pharmaceutical researchers, scientists, and professionals; students and educators in pharmacology and computational biology.

Novel pH and ammonia-sensitive intelligent film was fabricated with carboxymethyl cellulose (CMC) as film-forming substrate, gum Arabic (GA) as enhancer, and anthocyanins from Cinnamomum camphora fruit peel waste (ANC.P) as indicator, antioxidant, and antimicrobial. The incorporation of ANC.P on CMC-GA (CG-ANC.P) film significantly increased the mechanical property, physical properties (swelling degree, moisture content, solubility, vapor barrier properties, and oil permeability resistance), color, opacity, morphological characteristics, melting, and bioactivities (antioxidant, antibacterial, biodegradable, and pH/ammonia-sensitive manners) without significant changes in the film thickness. Computational molecular imitation analysis suggested an enhancement between ANC.P, CMC, and GA through hydrogen bonds and forces of van der Waals. Notably, the smart packaging film was tested to monitor soybean oil freshness during storage at 50 °C for 28 days. Moreover, the shelf-life characteristics proved the ability of films to retard oil oxidation. Additionally, the sensory characteristics emphasize higher scores of colors, odor, and overall acceptability with the increased concentration of anthocyanin. When applied in monitoring beef freshness at 4 °C, the CG-ANC.P film indicated sensitively with vision recognizable color changes from original pink to pink-yellow and finally to grayish-yellow, which highly correlated with the deterioration indexes of the total color difference, pH value, and total viable count of beef.