Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual\'s tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation\'s effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/ .Scientific contributionThis work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application.

Organophosphates constitute a major class of pesticides widely employed in agriculture to manage insect pests. Their toxicity is attributed to their ability to inhibit the functioning of acetylcholinesterase (AChE), an essential enzyme for normal nerve transmission. Organophosphates, especially chlorpyrifos, have been a key component of the integrated pest management (IPM) in onions, effectively controlling onion maggot Delia antiqua, a severe pest of onions. However, the growing concerns over the use of this insecticide on human health and the environment compelled the need for an alternative organophosphate and a potential microbial agent for bioremediation to mitigate organophosphate pesticide pollution. In the present study, chloropyrifos along with five other organophosphate insecticides, phosmet, primiphos-methyl, isofenphos, iodofenphos and tribuphos, were screened against the target protein AChE of D. antiqua using molecular modeling and docking techniques. The results revealed that iodofenphos showed the best interaction, while tribuphos had the lowest interaction with the AChE based on comparative binding energy values. Further, protein-protein interaction analysis conducted using the STRING database and Cytoscap software revealed that AChE is linked with a network of 10 different proteins, suggesting that the function of AChE is disrupted through interaction with insecticides, potentially leading to disruption within the network of associated proteins. Additionally, an in silico study was conducted to predict the binding efficiency of two organophosphate degrading enzymes, organophosphohydrolase (OpdA) from Agrobacterium radiobacter and Trichoderma harzianum paraoxonase 1 like (ThPON1-like) protein from Trichoderma harzianum, with the selected insecticides. The analysis revealed their potential to degrade the pesticides, offering a promising alternative before going for cumbersome onsite remediation.

We previously reported on the interaction of 10-chloro-7H-benzo[de]benzo[4,5]imidazo[2,1-a]isoquinolin-7-one (10-Cl-BBQ) with the Aryl hydrocarbon Receptor (AhR) and selective growth inhibition in breast cancer cell lines. We now report on a library of BBQ analogues with substituents on the phenyl and naphthyl rings for biological screening. Herein, we show that absence of the phenyl Cl of 10-Cl-BBQ to produce the simple BBQ molecule substantially enhanced the growth inhibitory effect with GI50 values of 0.001-2.1 μM in select breast cancer cell lines MCF-7, T47D, ZR-75-1, SKBR3, MDA-MB-468, BT20, BT474 cells, while having modest effects of 2.1-7 μM in other cell lines including HT29, U87, SJ-G2, A2780, DU145, BE2-C, MIA, MDA-MB-231 or normal breast cells, MCF10A (3.2 μM). The most potent growth inhibitory effect of BBQ was observed in the triple negative cell line, MDA-MB-468 with a GI50 value of 0.001 μM, presenting a 3,200-fold greater response than in the normal MCF10A breast cells. Additions of Cl, CH3, CN to the phenyl ring and ring expansion from benzoimidazole to dihydroquinazoline hindered the growth inhibitory potency of the BBQ analogues by blocking potential sites of CYP1 oxidative metabolism, while addition of Cl or NO2 to the naphthyl rings restored potency. In a cell-based reporter assay all analogues induced 1.2 to 10-fold AhR transcription activation. Gene expression analysis confirmed the induction of CYP1 oxygenases by BBQ. The CYP1 inhibitor α-naphthoflavone, and the SULT1A1 inhibitor quercetin significantly reduced the growth inhibitory effect of BBQ, confirming the importance of both phase I and II metabolic activation for growth inhibition. Conventional molecular modelling/docking revealed no significant differences between the binding poses of the most and least active analogues. More detailed DFT analysis at the DSD-PBEP86/Def-TZVPP level of theory could not identify significant geometric or electronic changes which would account for this varied AhR activation. Generation of Fukui functions at the same level of theory showed that CYP1 metabolism will primarily occur at the phenyl head group of the analogues, and substituents within this ring lead to lower cytotoxicity.

BACKGROUND: HIV-1 produces Tat, a crucial protein for transcription, viral replication, and CNS neurotoxicity. Tat interacts with TAR, enhancing HIV reverse transcription. Subtype C Tat variants (C31S, R57S, Q63E) are associated with reduced transactivation and neurovirulence compared to subtype B. However, their precise impact on Tat-TAR binding is unclear. This study investigates how these substitutions affect Tat-TAR interaction.
METHODS: We utilized molecular modelling techniques, including MODELLER, to produce precise three-dimensional structures of HIV-1 Tat protein variants. We utilized Tat subtype B as the reference or wild type, and generated Tat variants to mirror those amino acid variants found in Tat subtype C. Subtype C-specific amino acid substitutions were selected based on their role in the neuropathogenesis of HIV-1. Subsequently, we conducted molecular docking of each Tat protein variant to TAR using HDOCK, followed by molecular dynamic simulations.
RESULTS: Molecular docking results indicated that Tat subtype B (TatWt) showed the highest affinity for the TAR element (-262.07), followed by TatC31S (-261.61), TatQ63E (-256.43), TatC31S/R57S/Q63E (-238.92), and TatR57S (-222.24). However, binding free energy analysis showed higher affinities for single variants TatQ63E (-349.2 ± 10.4 kcal/mol) and TatR57S (-290.0 ± 9.6 kcal/mol) compared to TatWt (-247.9 ± 27.7 kcal/mol), while TatC31S and TatC31S/R57SQ/63E showed lower values. Interactions over the protein trajectory were also higher for TatQ63E and TatR57S compared to TatWt, TatC31S, and TatC31S/R57SQ/63E, suggesting that modifying amino acids within the Arginine/Glutamine-rich region notably affects TAR interaction. Single amino acid mutations TatR57S and TatQ63E had a significant impact, while TatC31S had minimal effect. Introducing single amino acid variants from TatWt to a more representative Tat subtype C (TatC31S/R57SQ/63E) resulted in lower predicted binding affinity, consistent with previous findings.
CONCLUSIONS: These identified amino acid positions likely contribute significantly to Tat-TAR interaction and the differential pathogenesis and neuropathogenesis observed between subtype B and subtype C. Additional experimental investigations should prioritize exploring the influence of these amino acid signatures on TAR binding to gain a comprehensive understanding of their impact on viral transactivation, potentially identifying them as therapeutic targets.

Plakophilin 1 (PKP1), a member of the p120ctn subfamily of the armadillo (ARM)-repeat-containing proteins, is an important structural component of cell-cell adhesion scaffolds although it can also be ubiquitously found in the cytoplasm and the nucleus. RYBP (RING 1A and YY1 binding protein) is a multifunctional intrinsically disordered protein (IDP) best described as a transcriptional regulator. Both proteins are involved in the development and metastasis of several types of tumors. We studied the binding of the armadillo domain of PKP1 (ARM-PKP1) with RYBP by using in cellulo methods, namely immunofluorescence (IF) and proximity ligation assay (PLA), and in vitro biophysical techniques, namely fluorescence, far-ultraviolet (far-UV) circular dichroism (CD), and isothermal titration calorimetry (ITC). We also characterized the binding of the two proteins by using in silico experiments. Our results showed that there was binding in tumor and non-tumoral cell lines. Binding in vitro between the two proteins was also monitored and found to occur with a dissociation constant in the low micromolar range (~10 μM). Finally, in silico experiments provided additional information on the possible structure of the binding complex, especially on the binding ARM-PKP1 hot-spot. Our findings suggest that RYBP might be a rescuer of the high expression of PKP1 in tumors, where it could decrease the epithelial-mesenchymal transition in some cancer cells.

BACKGROUND: Irritable bowel syndrome (IBS) is one of the most frequent and debilitating conditions leading to gastroenterological referrals. However, recommended treatments remain limited, yielding only limited therapeutic gains. Chitin-glucan (CG) is a novel dietary prebiotic classically used in humans at a dosage of 1.5-3.0 g/d and is considered a safe food ingredient by the European Food Safety Authority. To provide an alternative approach to managing patients with IBS, we performed preclinical molecular, cellular, and animal studies to evaluate the role of chitin-glucan in the main pathophysiological mechanisms involved in IBS.
OBJECTIVE: To evaluate the roles of CG in visceral analgesia, intestinal inflammation, barrier function, and to develop computational molecular models.
METHODS: Visceral pain was recorded through colorectal distension (CRD) in a model of long-lasting colon hypersensitivity induced by an intra-rectal administration of TNBS [15 milligrams (mg)/kilogram (kg)] in 33 Sprague-Dawley rats. Intracolonic pressure was regularly assessed during the 9 wk-experiment (weeks 0, 3, 5, and 7) in animals receiving CG (n = 14) at a human equivalent dose (HED) of 1.5 g/d or 3.0 g/d and compared to negative control (tap water, n = 11) and positive control (phloroglucinol at 1.5 g/d HED, n = 8) groups. The anti-inflammatory effect of CG was evaluated using clinical and histological scores in 30 C57bl6 male mice with colitis induced by dextran sodium sulfate (DSS) administered in their drinking water during 14 d. HT-29 cells under basal conditions and after stimulation with lipopolysaccharide (LPS) were treated with CG to evaluate changes in pathways related to analgesia (µ-opioid receptor (MOR), cannabinoid receptor 2 (CB2), peroxisome proliferator-activated receptor alpha, inflammation [interleukin (IL)-10, IL-1b, and IL-8] and barrier function [mucin 2-5AC, claudin-2, zonula occludens (ZO)-1, ZO-2] using the real-time PCR method. Molecular modelling of CG, LPS, lipoteichoic acid (LTA), and phospholipomannan (PLM) was developed, and the ability of CG to chelate microbial pathogenic lipids was evaluated by docking and molecular dynamics simulations. Data were expressed as the mean ± SEM.
RESULTS: Daily CG orally-administered to rats or mice was well tolerated without including diarrhea, visceral hypersensitivity, or inflammation, as evaluated at histological and molecular levels. In a model of CRD, CG at a dosage of 3 g/d HED significantly decreased visceral pain perception by 14% after 2 wk of administration (P < 0.01) and reduced inflammation intensity by 50%, resulting in complete regeneration of the colonic mucosa in mice with DSS-induced colitis. To better reproduce the characteristics of visceral pain in patients with IBS, we then measured the therapeutic impact of CG in rats with TNBS-induced inflammation to long-lasting visceral hypersensitivity. CG at a dosage of 1.5 g/d HED decreased visceral pain perception by 20% five weeks after colitis induction (P < 0.01). When the CG dosage was increased to 3.0 g/d HED, this analgesic effect surpassed that of the spasmolytic agent phloroglucinol, manifesting more rapidly within 3 wk and leading to a 50% inhibition of pain perception (P < 0.0001). The underlying molecular mechanisms contributing to these analgesic and anti-inflammatory effects of CG involved, at least in part, a significant induction of MOR, CB2 receptor, and IL-10, as well as a significant decrease in pro-inflammatory cytokines IL-1b and IL-8. CG also significantly upregulated barrier-related genes including muc5AC, claudin-2, and ZO-2. Molecular modelling of CG revealed a new property of the molecule as a chelator of microbial pathogenic lipids, sequestering gram-negative LPS and gram-positive LTA bacterial toxins, as well as PLM in fungi at the lowesr energy conformations.
CONCLUSIONS: CG decreased visceral perception and intestinal inflammation through master gene regulation and direct binding of microbial products, suggesting that CG may constitute a new therapeutic strategy for patients with IBS or IBS-like symptoms.

The presence of antibiotics in soils is increasing drastically in last decades due to the intensive farming industry and excessive human consumption. Clay minerals are one of the soil components with great adsorption capacity for organic pollutants. The study of interactions between antibiotics and mineral surfaces will give us scientific knowledge of these pollutants through soils. In this work, we study the adsorption of the antibiotic ciprofloxacin in the clay mineral fraction of soils from the Argentinian zone of Santa Rosa (Corrientes), in a collaborative research of experiments and atomistic modelling calculations of the intercalation of ciprofloxacin in the interlayer space of montmorillonite. Adsorption and desorption isotherms were performed and compared with different isotherm models. Additionally, enthalpy, entropy, and free energy were determined from equilibrium constants at a function of temperature. All these experiments and calculations lead to the conclusions that two adsorption types of ciprofloxacin are found on clay minerals: one weakly sorbed that is released during the desorption experiments, and other one strongly joined that remains in the soil.

This study focused on the efficacy of a calcined layered double hydroxide (CLDH) clay in adsorbing two antiretroviral drugs (ARVDs), namely efavirenz (EFV) and nevirapine (NVP), from wastewater. The clay was synthesized using the co-precipitation method, followed by subsequent calcination in a muffle furnace at 500 °C for 4 h. The neat and calcined clay samples were subjected to various characterization techniques to elucidate their physical and chemical properties. Response surface modelling (RSM) was used to evaluate the interactions between the solution\'s initial pH, adsorbent loading, reaction temperature, and initial pollutant concentration. Additionally, the adsorption kinetics, thermodynamics, and reusability of the adsorbent were evaluated. The results demonstrated that NVP exhibited a faster adsorption rate than EFV, with both reaching equilibrium within 20-24 h. The pseudo-second order (PSO) model provided a good fit for the kinetics data. Thermodynamics analysis revealed that the adsorption process was spontaneous and exothermic, predominantly governed by physisorption interactions. The adsorption isotherms followed the Freundlich model, and the maximum adsorption capacities for EFV and NVP were established to be 2.73 mg/g and 2.93 mg/g, respectively. Evaluation of the adsorption mechanism through computational analysis demonstrated that both NVP and EFV formed stable complexes with CLDH, with NVP exhibiting a higher affinity. The associated adsorption energies were established to be -731.78 kcal/mol for NVP and -512.6 kcal/mol for EFV. Visualized non-covalent interaction (NCI) graphs indicated that hydrogen bonding played a significant role in ARVDs-CLDH interactions, further emphasizing physisorption as the dominant adsorption mechanism.

The rise of pyrazinamide (PZA)-resistant strains of Mycobacterium tuberculosis (MTB) poses a major challenge to conventional tuberculosis (TB) treatments. PZA, a cornerstone of TB therapy, must be activated by the mycobacterial enzyme pyrazinamidase (PZase) to convert its active form, pyrazinoic acid, which targets the ribosomal protein S1. Resistance, often associated with mutations in the RpsA protein, complicates treatment and highlights a critical gap in the understanding of structural dynamics and mechanisms of resistance, particularly in the context of the G97D mutation. This study utilizes a novel integration of computational techniques, including multiscale biomolecular and molecular dynamics simulations, physicochemical and medicinal chemistry predictions, quantum computations and virtual screening from the ZINC and Chembridge databases, to elucidate the resistance mechanism and identify lead compounds that have the potential to improve treatment outcomes for PZA-resistant MTB, namely ZINC15913786, ZINC20735155, Chem10269711, Chem10279789 and Chem10295790. These computational methods offer a cost-effective, rapid alternative to traditional drug trials by bypassing the need for organic subjects while providing highly accurate insight into the binding sites and efficacy of new drug candidates. The need for rapid and appropriate drug development emphasizes the need for robust computational analysis to justify further validation through in vitro and in vivo experiments.

The advent of deep learning algorithms for protein folding opened a new era in the ability of predicting and optimizing the function of proteins once the sequence is known. The task is more intricate when cofactors like metal ions or small ligands are essential to functioning. In this case, the combined use of traditional simulation methods based on interatomic force fields and deep learning predictions is mandatory. We use the example of [FeFe] hydrogenases, enzymes of unicellular algae promising for biotechnology applications to illustrate this situation. [FeFe] hydrogenase is an iron-sulfur protein that catalyzes the chemical reduction of protons dissolved in liquid water into molecular hydrogen as a gas. Hydrogen production efficiency and cell sensitivity to dioxygen are important parameters to optimize the industrial applications of biological hydrogen production. Both parameters are related to the organization of iron-sulfur clusters within protein domains. In this work, we propose possible three-dimensional structures of Chlorella vulgaris 211/11P [FeFe] hydrogenase, the sequence of which was extracted from the recently published genome of the given strain. Initial structural models are built using: (i) the deep learning algorithm AlphaFold; (ii) the homology modeling server SwissModel; (iii) a manual construction based on the best known bacterial crystal structure. Missing iron-sulfur clusters are included and microsecond-long molecular dynamics of initial structures embedded into the water solution environment were performed. Multiple-walkers metadynamics was also used to enhance the sampling of structures encompassing both functional and non-functional organizations of iron-sulfur clusters. The resulting structural model provided by deep learning is consistent with functional [FeFe] hydrogenase characterized by peculiar interactions between cofactors and the protein matrix.