Contribution of collagen degradation and proteoglycan depletion to cartilage degeneration in primary and secondary osteoarthritis: an in silico study.

OBJECTIVES: Current experimental approaches cannot elucidate the effect of maladaptive changes on the main cartilage constituents during the degeneration process in osteoarthritis (OA). In silico approaches, however, allow creating 'virtual knock-out' cases to elucidate these effects in a constituent-specific manner. We used such an approach to study the main mechanisms of cartilage degeneration in different mechanical loadings associated with the following OA etiologies: (1) physiological loading of degenerated cartilage, (2) injurious loading of healthy intact cartilage and (3) physiological loading of cartilage with a focal defect. METHODS: We used the recently developed Cartilage Adaptive REorientation Degeneration (CARED) framework to simulate cartilage degeneration associated with primary and secondary OA (OA cases (1)-(3)). CARED incorporates numerical description of tissue-level cartilage degeneration mechanisms in OA, namely, collagen degradation, collagen reorientation, fixed charged density loss and tissue hydration increase following mechanical loading. We created 'virtual knock-out' scenarios by deactivating these degenerative processes one at a time in each of the three OA cases. RESULTS: In the injurious loading of intact and physiological loading of degenerated cartilage, collagen degradation drives degenerative changes through fixed charge density loss and tissue hydration rise. In contrast, the two later mechanisms were more prominent in the focal defect cartilage model. CONCLUSION: The virtual knock-out models reveal that injurious loading to intact cartilage and physiological loading to degenerated cartilage induce initial degenerative changes in the collagen network, whereas, in the presence of a focal cartilage defect, mechanical loading initially causes proteoglycans (PG) depletion, before changes in the collagen fibril network occur.

A Bibliometric and In Silico-Based Analysis of Anti-Lung Cancer Compounds from Sea Cucumber.

Lung cancer is one of the most lethal malignancies in the world. However, current curative approaches for treating this type of cancer have some weaknesses. Therefore, scientists are attempting to discover new anti-lung cancer agents. Sea cucumber is a marine-derived source for discovering biologically active compounds with anti-lung cancer properties. To explore the anti-lung cancer properties of sea cucumber, we analyzed surveys using VOSviewer software and identified the most frequently used keywords. We then searched the Google Scholar database for compounds with anti-lung cancer properties within that keyword family. Finally, we used AutoDock 4 to identify the compounds with the highest affinity for apoptotic receptors in lung cancer cells. The results showed that triterpene glucosides were the most frequently identified compounds in studies examining the anti-cancer properties of sea cucumbers. Intercedenside C, Scabraside A, and Scabraside B were the three triterpene glycosides with the highest affinity for apoptotic receptors in lung cancer cells. To the best of our knowledge, this is the first time that anti-lung cancer properties of sea cucumber-derived compounds have been examined in in silico conditions. Ultimately, these three components displayed anti-lung cancer properties in in silico conditions and may be used for the manufacture of anti-lung cancer agents in the near future.

Thermodynamic and structural profiles of multi-target binding of vinblastine in solution.

Structural information about drug-receptor interactions is paramount in drug discovery and subsequent optimization processes. Drugs can bind to multiple potential targets as they contain common chemical entities in their structures. Understanding the details of such interactions offer possibilities for repurposing and developing potent inhibitors of disease pathways. Vinblastine (VLB) is a potent anticancer molecule showing multiple receptor interactions with different affinities and degrees of structural perturbations. We have investigated the multi-target binding profile of VLB with DNA and human serum albumin (HSA) in a dynamic physiological environment using spectroscopic, molecular dynamics simulations, and quantum mechanical calculations to evaluate the structural features, mode, ligand and receptor flexibility, and energetics of complexation. These results confirm that VLB prefers to bind in the major groove of DNA with some inclination toward Thymidine residue and the TR-5 binding site in HSA with its catharanthine half making important contacts with both the receptors. Spectroscopic investigation at multiple temperatures has also proved that VLB binding is entropy driven indicating the major groove and TR-5 binding site of interaction. Finally, the overall binding is facilitated by van der Waals contacts and a few conventional H-bonds. VLB portrays reasonable conformational diversity on binding with multiple receptors.

The Application of In Silico Methods on Umami Taste Receptor.

The umami taste receptor is a heterodimer composed of two members of the T1R taste receptor family: T1R1 (taste receptor type 1 member 1) and T1R3 (taste receptor type 1 member 3). Taste receptor T1R1-T1R3 can be activated, or modulated, by binding to several natural ligands, such as L-glutamate, inosine-5'-monophosphate (IMP), and guanosine-5'-monophosphate (GMP). Because no structure of the umami taste receptor has been solved until now, in silico techniques, such as homology modelling, molecular docking, and molecular dynamics (MD) simulations, are used to generate a 3D structure model of this receptor and to understand its molecular mechanisms. The purpose of this chapter is to highlight how computational methods can provide a better deciphering of the mechanisms of action of umami ligands in activating the umami taste receptors leading to advancements in the taste research field.

Leveraging computational tools to combat malaria: assessment and development of new therapeutics.

As the world grapples with the relentless challenges posed by diseases like malaria, the advent of sophisticated computational tools has emerged as a beacon of hope in the quest for effective treatments. In this study we delve into the strategies behind computational tools encompassing virtual screening, molecular docking, artificial intelligence (AI), and machine learning (ML). We assess their effectiveness and contribution to the progress of malaria treatment. The convergence of these computational strategies, coupled with the ever-increasing power of computing systems, has ushered in a new era of drug discovery, holding immense promise for the eradication of malaria. SCIENTIFIC CONTRIBUTION: Computational tools remain pivotal in drug design and development. They provide a platform for researchers to explore various treatment options and save both time and money in the drug development pipeline. It is imperative to assess computational techniques and monitor their effectiveness in disease control. In this study we examine renown computational tools that have been employed in the battle against malaria, the benefits and challenges these tools have presented, and the potential they hold in the future eradication of the disease.

In Silico Identification of Dysregulated miRNAs Targeting KRAS Gene in Pancreatic Cancer.

Pancreatic cancer (PC) is highly lethal, with KRAS mutations in up to 95% of cases. miRNAs inversely correlate with KRAS expression, indicating potential as biomarkers. This study identified miRNAs targeting KRAS and their impact on PC characteristics using in silico methods. dbDEMC identified dysregulated miRNAs in PC; TargetScan, miRDB, and PolymiRTS 3.0 identified miRNAs specific for the KRAS gene; and OncomiR evaluated the association of miRNAs with clinical characteristics and survival in PC. The correlation between miRNAs and KRAS was analysed using ENCORI/starBase. A total of 210 deregulated miRNAs were identified in PC (116 overexpressed and 94 underexpressed). In total, 16 of them were involved in the regulation of KRAS expression and 9 of these (hsa-miR-222-3p, hsa-miR-30a-5p, hsa-miR-30b-5p, hsa-miR-30e-5p, hsa-miR-377-3p, hsa-miR-495-3p, hsa-miR-654-3p, hsa-miR-877-5p and hsa-miR-885-5p) were associated with the clinical characteristics of the PC. Specifically, the overexpression of hsa-miR-30a-5p was associated with PC mortality, and hsa-miR-30b-5p, hsa-miR-377-3p, hsa-miR-495-3p, and hsa-miR-885-5p were associated with survival. Correlation analysis revealed that the expression of 10 miRNAs is correlated with KRAS expression. The dysregulated miRNAs identified in PC may regulate KRAS and some are associated with clinically relevant features, highlighting their potential as biomarkers and therapeutic targets in PC treatment. However, experimental validation is required for confirmation.

Computational investigation of turmeric phytochemicals targeting PTR1 enzyme of Leishmania species.

In this study, we used in silico techniques to identify available parasite treatments, representing a promising therapeutic avenue. Building upon our computational initiatives aimed at discovering natural inhibitors for various target enzymes from parasites causing neglected tropical diseases (NTDs), we present novel findings on three turmeric-derived phytochemicals as inhibitors of Leishmania pteridine reductase I (PTR1) through in silico methodologies. PTR1, a crucial enzyme in the unique folate metabolism of trypanosomatid parasites, holds established therapeutic significance. Employing MOE software, a molecular docking analysis assesses the efficacy of turmeric phytochemicals against Leishmania PTR1. Validation of the docking protocol is confirmed with an RMSD value of 2. Post-docking, compounds displaying notable interactions with critical residues and binding affinities ranging between -6 and -8 kcal/mol are selected for interaction pattern exploration. Testing twelve turmeric phytochemicals, including curcumin, zingiberene, curcumol, curcumenol, eugenol, bisdemethoxycurcumin, tetrahydrocurcumin, tryethylcurcumin, turmerones, turmerin, demethoxycurcumin, and turmeronols, revealed binding affinities ranging from -5.5 to -8 kcal/mol. Notably, curcumin, demethoxycurcumin, and bisdemethoxycurcumin exhibit binding affinities within -6.5 to -8 kcal/mol and establish substantial interactions with catalytic residues. These phytochemicals hold promise as lead structures for rational drug design targeting Leishmania spp. PTR in future applications. This work underscores the potential of these identified phytochemicals in the development of more effective inhibitors, demonstrating their relevance in addressing neglected tropical diseases caused by parasites.

Computational exploration and design of a multi-epitopes vaccine construct against Chlamydia psittaci.

Chlamydia psittaci is an intracellular pathogen and causes variety of deadly infections in humans. Antibiotics are effective against C. psittaci however high percentage of resistant strains have been reported in recent times. As there is no licensed vaccine, we used in-silico techniques to design a multi-epitopes vaccine against C. psittaci. Following a step-wise protocol, the proteome of available 26 strains was retrieved and filtered for subcellular localized proteins. Five proteins were selected (2 extracellular and 3 outer membrane) and were further analyzed for B-cell and T-cell epitopes prediction. Epitopes were further checked for antigenicity, solubility, stability, toxigenicity, allergenicity, and adhesive properties. Filtered epitopes were linked via linkers and the 3D structure of the designed vaccine construct was predicted. Binding of the designed vaccine with immune receptors: MHC-I, MHC-II, and TLR-4 was analyzed, which resulted in docking energy scores of -4.37 kcal/mol, -0.20 kcal/mol and -22.38 kcal/mol, respectively. Further, the docked complexes showed stable dynamics with a maximum value of vaccine-MHC-I complex (7.8 Å), vaccine-MHC-II complex (6.2 Å) and vaccine-TLR4 complex (5.2 Å). As per the results, the designed vaccine construct reported robust immune responses to protect the host against C. psittaci infections. In the study, the C. psittaci proteomes were considered in pan-genome analysis to extract core proteins. The pan-genome analysis was conducted using bacterial pan-genome analysis (BPGA) software. The core proteins were checked further for non-redundant proteins using a CD-Hit server. Surface localized proteins were investigated using PSORTb v 3.0. The surface proteins were BLASTp against Virulence Factor Data Base (VFDB) to predict virulent factors. Antigenicity prediction of the shortlisted proteins was further done using VAXIGEN v 2.0. The epitope mapping was done using the immune epitope database (IEDB). A multi-epitopes vaccine was built and a 3D structure was generated using 3Dprot online server. The docking analysis of the designed vaccine with immune receptors was carried out using PATCHDOCK. Molecular dynamics and post-simulation analyses were carried out using AMBER v20 to decipher the dynamics stability and intermolecular binding energies of the docked complexes.Communicated by Ramaswamy H. Sarma.

Targeting non-coding RNAs: Perspectives and challenges of in-silico approaches.

The growing information currently available on the central role of non-coding RNAs (ncRNAs) including microRNAs (miRNAS) and long non-coding RNAs (lncRNAs) for chronic and degenerative human diseases makes them attractive therapeutic targets. RNAs carry out different functional roles in human biology and are deeply deregulated in several diseases. So far, different attempts to therapeutically target the 3D RNA structures with small molecules have been reported. In this scenario, the development of computational tools suitable for describing RNA structures and their potential interactions with small molecules is gaining more and more interest. Here, we describe the most suitable strategies to study ncRNAs through computational tools. We focus on methods capable of predicting 2D and 3D ncRNA structures. Furthermore, we describe computational tools to identify, design and optimize small molecule ncRNA binders. This review aims to outline the state of the art and perspectives of computational methods for ncRNAs over the past decade.

Designing multi-target drugs for the treatment of major depressive disorder.

INTRODUCTION: Major depressive disorders (MDD) pose major health burdens globally. Currently available medications have their limitations due to serious adverse effects, long latency periods as well as resistance. Considering the highly complicated pathological nature of this disorder, it has been suggested that multitarget drugs or multi-target-directed ligands (MTDLs) may provide long-term therapeutic solutions for the treatment of MDD. AREAS COVERED: In the current review, recent lead design and lead modification strategies have been covered. Important investigations reported in the last ten years (2013-2022) for the preclinical development of MTDLs (through synthetic medicinal chemistry and biological evaluation) for the treatment of MDD were discussed as case studies to focus on the recent design strategies. The discussions are categorized on the basis of pharmacological targets. Based on these important case studies, the challenges involved in different design strategies were discussed in detail. EXPERT OPINION: Even though large variations were observed in the selection of pharmacological targets, some potential biological targets (NMDA, melatonin receptors) are required to be explored extensively for the design of MTDLs. Similarly, apart from structure activity relationship (SAR), in silico techniques such as multitasking cheminformatic modeling, molecular dynamics simulation and virtual screening should be exploited to a greater extent.

Waste to Medicine: Evidence from Computational Studies on the Modulatory Role of Corn Silk on the Therapeutic Targets Implicated in Type 2 Diabetes Mellitus.

Type 2 diabetes mellitus (T2DM) is characterized by insulin resistance and/or defective insulin production in the human body. Although the antidiabetic action of corn silk (CS) is well-established, the understanding of the mechanism of action (MoA) behind this potential is lacking. Hence, this study aimed to elucidate the MoA in different samples (raw and three extracts: aqueous, hydro-ethanolic, and ethanolic) as a therapeutic agent for the management of T2DM using metabolomic profiling and computational techniques. Ultra-performance liquid chromatography-mass spectrometry (UP-LCMS), in silico techniques, and density functional theory were used for compound identification and to predict the MoA. A total of 110 out of the 128 identified secondary metabolites passed the Lipinski's rule of five. The Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analysis revealed the cAMP pathway as the hub signaling pathway, in which ADORA1, HCAR2, and GABBR1 were identified as the key target genes implicated in the pathway. Since gallicynoic acid (-48.74 kcal/mol), dodecanedioc acid (-34.53 kcal/mol), and tetradecanedioc acid (-36.80 kcal/mol) interacted well with ADORA1, HCAR2, and GABBR1, respectively, and are thermodynamically stable in their formed compatible complexes, according to the post-molecular dynamics simulation results, they are suggested as potential drug candidates for T2DM therapy via the maintenance of normal glucose homeostasis and pancreatic ß-cell function.

An in silico Framework of Cartilage Degeneration That Integrates Fibril Reorientation and Degradation Along With Altered Hydration and Fixed Charge Density Loss.

Injurious mechanical loading of articular cartilage and associated lesions compromise the mechanical and structural integrity of joints and contribute to the onset and progression of cartilage degeneration leading to osteoarthritis (OA). Despite extensive in vitro and in vivo research, it remains unclear how the changes in cartilage composition and structure that occur during cartilage degeneration after injury, interact. Recently, in silico techniques provide a unique integrated platform to investigate the causal mechanisms by which the local mechanical environment of injured cartilage drives cartilage degeneration. Here, we introduce a novel integrated Cartilage Adaptive REorientation Degeneration (CARED) algorithm to predict the interaction between degenerative variations in main cartilage constituents, namely collagen fibril disorganization and degradation, proteoglycan (PG) loss, and change in water content. The algorithm iteratively interacts with a finite element (FE) model of a cartilage explant, with and without variable depth to full-thickness defects. In these FE models, intact and injured explants were subjected to normal (2 MPa unconfined compression in 0.1 s) and injurious mechanical loading (4 MPa unconfined compression in 0.1 s). Depending on the mechanical response of the FE model, the collagen fibril orientation and density, PG and water content were iteratively updated. In the CARED model, fixed charge density (FCD) loss and increased water content were related to decrease in PG content. Our model predictions were consistent with earlier experimental studies. In the intact explant model, minimal degenerative changes were observed under normal loading, while the injurious loading caused a reorientation of collagen fibrils toward the direction perpendicular to the surface, intense collagen degradation at the surface, and intense PG loss in the superficial and middle zones. In the injured explant models, normal loading induced intense collagen degradation, collagen reorientation, and PG depletion both on the surface and around the lesion. Our results confirm that the cartilage lesion depth is a crucial parameter affecting tissue degeneration, even under physiological loading conditions. The results suggest that potential fibril reorientation might prevent or slow down fibril degradation under conditions in which the tissue mechanical homeostasis is perturbed like the presence of defects or injurious loading.

Identification of New Ocellatin Antimicrobial Peptides by cDNA Precursor Cloning in the Frame of This Family of Intriguing Peptides.

Ocellatins are a family of antimicrobial peptides found exclusively in the Leptodactylus genus. To date, 10 species have been studied and more than 23 peptides described. Here we report the sequences of five new peptides from the skin of the frog Leptodactylus latrans (Anura: Leptodactylidae) determined by cDNA cloning of the complete prepro-peptide structures. The mature peptides were characterized with in silico tools and compared with those previously described. With 21 amino acid residues, this new set of peptides not previously described in the Leptodactylus genus share between 100 and 76.2% similarity to ocellatin antimicrobial peptides. These novel peptides are cationic and their three-dimensional (3D) structure holds the highly conserved residues G1, D4, K7, and K11 and a high theoretical amphipathic α-helix content. Furthermore, in silico analyses of these new peptides predicted antimicrobial activity. This study is framed in the context of previous work published about ocellatins, and therefore, provides a review of this intriguing family of peptides.

Integrating bioassays, chemical analysis and in silico techniques to identify genotoxicants in surface water.

Identification of hazardous compounds, as the first step of water protection and regulation, is still challenged by the difficulty to establish a linkage between toxic effects and suspected contaminants. Genotoxic compounds are one type of highly relevant toxicants in surface water, which may attack the DNA and lead to cancer in individual organism, or even damaged germ cells to be passed on to future generations. Thus, the establishment of a linkage between genotoxic effects and genotoxicant is important for environmental toxicologists and chemists. For this purpose, in the present study in silico methods were integrated with bioassays, chemical analysis and literature information to identify genotoxicants in surface water. Large volume water samples from 22 sampling sites of the Danube were collected and subjected to biological and chemical analysis. Samples from the most toxic sites (JDS32, JDS44 and JDS63) induced significant genotoxic effects in the micronucleus assay, and two of them caused mutagenicity in the Ames fluctuation assay. Chemical analysis showed that 68 chemicals were detected in these most toxic samples. Literature findings and in silico techniques using the OECD QSAR Toolbox and the ChemProp software package revealed genotoxic potentials for 29 compounds out of 68 targeted chemicals. To confirm the integrative technical data, the micronucleus assay and the Ames fluctuation assay were applied with artificial mixtures of those compounds and the raw water sample extracts. The results showed that 18 chemicals explained 48.5% of the genotoxicity in the micronucleus assay. This study highlights the capability of in silico techniques in linking adverse biological effect to suspicious hazardous compounds for the identification of toxicity drivers, and demonstrates the genotoxic potential of pollutants in the Danube.