In vitro assays identified thiohydantoins with anti-trypanosomatid activity and molecular modelling studies indicated possible selective CYP51 inhibition.

This work investigates the anti-trypanosomal activities of ten thiohydantoin derivatives against the parasite Trypanosoma cruzi. Compounds with aliphatic chains (THD1, THD3, and THD5) exhibited the most promising IC50 against the epimastigote form of T. cruzi. Also, it showed lower cytotoxicity to mammalian cells. THD3 and THD5 (IC50 = 72.4 µg/mL and 115 µg/mL) presented great activity against trypomastigote and amastigote forms (IC50 = 47.7 µg/mL and 34.1 µg/mL). THD5 had high selectivity index (SI = 15.1) against the amastigote form. The molecular docking and molecular dynamics simulations were performed to understand the interaction between the THD and the important target CYP51 enzyme essential to T. cruzi. THD3 and THD5 were found to have strong interactions within the hydrophobic channel of CYP51 due to their aliphatic side chains, leading to favorable binding free energies. Despite the possibility of cross-reactivity between THD5 and human CYP2C9, the results indicate low identity and similarity between the homolog enzymes and possible selectivity of THD5 for the protozoan one, suggesting that these compounds could inhibit sterol biosynthesis, crucial for the parasite's survival​. These findings indicate that THD3 and THD5 are promising hits for the development of Chagas disease treatments. To fully validate this potential, carrying out enzymatic and other in vitro and in vivo assays is essential in the future.

How does dopamine convert into norepinephrine? Insights on the key step of the reaction.

CONTEXT: Dopamine ß -monooxygenase (D ß M) is an essential enzyme in the organism that regioselectively converts dopamine into R-norepinephrine, the key step of the reaction, studied in this paper, is a hydrogen atom transfer (HAT) from dopamine to a superoxo complex on D ß M, forming a hydroperoxo intermediate and dopamine radical. It was found that the formation of a hydrogen bond between dopamine and the D ß M catalyst strengthens the substrate-enzyme interaction and facilitates the HAT which takes place selectively to give the desired enantiomeric form of the product. Six reactions leading to the hydroperoxo intermediate were analyzed in detail using theoretical and computational tools in order to identify the most probable reaction mechanism. The reaction force analysis has been used to demonstrate that the nature of the activation energy is mostly structural and largely due to the initial approach of species in order to get closer to each other to facilitate the hydrogen abstraction. On the other hand, the reaction electronic flux revealed that electronic activity driving the reactions is triggered by polarization effects and, in the most probable reaction among the six studied, it takes place in a concerted and non-spontaneous way. Chemical events driving the reaction have been identified and the energy absorbed or delivered by each one was quantified in detail. METHODS: The dopamine and a computational model of the copper superoxo complex on D ß M were optimized at B3LYP-D3(BJ)/6-311 G(d,p) level theory in the Gaussian 16 software package. Optimization and IRC calculations were performed in the gas phase and through the PCM solvation model to mimic the protein medium. Non-covalent interactions were plotted using the NCI-plot software.

Shape-dependent CO chemisorption on Pt13 nanocluster deposited on reduced TiO2(110).

This article delves into the impact of nanoparticle shape on CO chemisorption and the reactivity of Pt13 nanocatalysts supported on reduced TiO2(110). Distinct reactivity in carbon monoxide adsorption is observed among nanoparticles, all composed of 13 platinum atoms but varying in shape. The calculated formation and CO adsorption energies are correlated to the electronic properties of the system and the oxidation states of the Pt atoms involved. Through an analysis of band shifting during the deposition of Pt clusters onto the oxide, and a comparison of the valence band maximum with the measured oxidation potential for the CO to CO2 reaction, we make predictions about the system's oxidation capability in this reaction. Our findings suggest that Pt13 clusters with cuboid, double triangle DT2 and octahedral Oh shapes, supported on the surface, are particularly advantageous for catalyzing the conversion of CO to CO2. Within these geometries, several configurations for CO adsorption are evaluated, focusing on the ratio between anionic and cationic Pt sites. This ratio appears to govern the activity for CO oxidation, aligning with recent experimental reports.

Structural Analysis of Variants of the Ferritin Light Chain Protein and Its Relationship with Neuroferritinopathy.

Ferritin is a highly conserved spherical protein that stores iron and possesses triple and quadruple symmetry input ports. Additionally, it is composed of light chains that can be affected by post-translational mutations, reducing the iron storage capacity in the brain and leading to neuroferritinopathy, which is a rare disease with limited bioinformatics data. In this study, we analyzed the biochemical mechanism of different ferritin mutations reported in the literature, through the characterization and determination of the in silico structural model by searching databases, implementing bioinformatics programs such as Jalview, NetNGlyc 1.0, NetOGlyc 3.1, and three-dimensional structure predictors with machine learning such as Alphafold, demonstrating the generation of hairpin and steric hindrances that hinder the aggregation of subunits and changes in the size and arrangement of quadruple and triple entry holes of the A96T mutation compared to the wild-type protein, since in the quadruple entry hole, a decrease in area is observed compared to the wild-type protein and the triple entry hole has a decrease in distance measurements of 6.504 Å. This possibly affects the functionality of the protein, thus releasing high concentrations of iron in the brain and causing neurodegeneration.

Revealing Missing Protein-Ligand Interactions Using AlphaFold Predictions.

Protein-ligand interactions represent an essential step to understand the bases of molecular recognition, an intense field of research in many scientific areas. Structural biology has played a central role in unveiling protein-ligand interactions, but current techniques are still not able to reliably describe the interactions of ligands with highly flexible regions. In this work, we explored the capacity of AlphaFold2 (AF2) to estimate the presence of interactions between ligands and residues belonging to disordered regions. As these interactions are missing in the crystallographic-derived structures, we called them "ghost interactions". Using a set of protein structures experimentally obtained after AF2 was trained, we found that the obtained models are good predictors of regions associated with order-disorder transitions. Additionally, we found that AF2 predicts residues making ghost interactions with ligands, which are mostly buried and show differential evolutionary conservation with the rest of the residues located in the flexible region. Our findings could fuel current areas of research that consider, given their biological relevance and their involvement in diseases, intrinsically disordered proteins as potentially valuable targets for drug development.

Benzocarbazoledinones as SARS-CoV-2 Replication Inhibitors: Synthesis, Cell-Based Studies, Enzyme Inhibition, Molecular Modeling, and Pharmacokinetics Insights.

Endemic and pandemic viruses represent significant public health challenges, leading to substantial morbidity and mortality over time. The COVID-19 pandemic has underscored the urgent need for the development and discovery of new, potent antiviral agents. In this study, we present the synthesis and anti-SARS-CoV-2 activity of a series of benzocarbazoledinones, assessed using cell-based screening assays. Our results indicate that four compounds (4a, 4b, 4d, and 4i) exhibit EC50 values below 4 µM without cytotoxic effects in Calu-3 cells. Mechanistic investigations focused on the inhibition of the SARS-CoV-2 main protease (Mpro) and papain-like protease (PLpro) have used enzymatic assays. Notably, compounds 4a and 4b showed Mpro inhibition activity with IC50 values of 0.11 ± 0.05 and 0.37 ± 0.05 µM, respectively. Furthermore, in silico molecular docking, physicochemical, and pharmacokinetic studies were conducted to validate the mechanism and assess bioavailability. Compound 4a was selected for preliminary drug-likeness analysis and in vivo pharmacokinetics investigations, which yielded promising results and corroborated the in vitro and in silico findings, reinforcing its potential as an anti-SARS-CoV-2 lead compound.

Mutational analysis of Phanerochaete chrysosporium´s purine transporter.

We present here a mutational analysis of the purine transporter from Phanerochaete chrysosporium (PhZ), a member of the AzgA-like subfamily within the Nucleobase Ascorbate Transporters family. We identified key residues that determine its substrate specificity and transport efficiency. Thirteen PhZ mutants were generated and heterologously expressed in Aspergillus nidulans. The growth of mutant strains in the presence of purines and toxic analogues and the uptake rate of radiolabelled hypoxanthine were evaluated. Results revealed that ten mutants showed differences in transport compared to the wild-type PhZ: six mutants completely lost function, two exhibited decreased transport activity, and two showed increased hypoxanthine uptake. Subcellular localization and expression level analyses indicated that the differences in transport activity were not due to trafficking issues to the plasma membrane or protein stability. A three-dimensional model of PhZ, constructed with the artificial intelligence-based AlphaFold2 program, suggested that critical residues for transport are located in transmembrane segments and an internal helix. In the latter, the A418 residue was identified as playing a pivotal role in transport efficiency despite being far from the putative substrate binding site, as mutant A418V showed an increased initial uptake efficiency for the transporter´s physiological substrates. We also report that residue L124, which lies in the putative substrate binding site, plays a critical role in substrate transport, emerging as an additional determinant in the transport mechanism of this family of transporters. These findings underscore the importance of specific residues in AzgA-like transporters and enhance our understanding of the intricate mechanisms governing substrate specificity and transport efficiency within this family.

Ramachandran-like Conformational Space for DNA.

DNA's ability to exist in a wide variety of structural forms, subforms, and secondary motifs is fundamental to numerous biological processes and has driven the development of biotechnological applications. Major determinants of DNA flexibility are the multiple torsional degrees of freedom around the phosphodiester backbone. This high complexity can be rationalized by using two pseudotorsional angles linking atoms P and C4', from which Ramachandran-like plots can be built. In this contribution, we explore the distribution of η (eta: C4'i-1-Pi-C4'i-Pi+1) and θ (theta: Pi-C4'i-Pi+1-C4'i+1) angles in known experimental structures retrieved from the Protein Data Bank (PDB), subdividing the conformational space into different datasets. After the removal of the canonical/helical conformations typical of the B-form, we find the existence of a conformational map with clearly permitted and forbidden regions. Some of these regions are populated with specific DNA forms, like Z- or A-DNA, or by specific secondary motifs, like G-quadruplexes and junctions. We evaluated the sequence dependency and energy relationship among the high-density regions identified in the η-θ space. Furthermore, we analyzed the effect produced by proteins and cations when bound to DNA, finding that specific proteins produce some nonhelical conformations, while other regions appear to be stabilized by divalent cations.

The phosphodiester dissociative hydrolysis of a DNA model promoted by metal dications.

CONTEXT: Phosphodiester bonds, which form the backbone of DNA, are highly stable in the absence of catalysts. This stability is crucial for maintaining the integrity of genetic information. However, when exposed to catalytic agents, these bonds become susceptible to cleavage. In this study, we investigated the role of different metal dications (Ca2⁺, Mg2⁺, Zn2⁺, Mn2⁺, and Cu2⁺) in promoting the hydrolysis of phosphodiester bonds. A minimal DNA model was constructed using two pyrimidine nucleobases (cytosine and thymine), two deoxyribose units, one phosphate group, and one metallic dication coordinated by six water molecules. The results highlight that Cu2⁺ is the most efficient in lowering the energy barrier for bond cleavage, with an energy barrier of 183 kJ/mol, compared to higher barriers for metals like Zn2⁺ (202 kJ/mol), Mn2⁺ (202 kJ/mol), Mg2⁺ (210 kJ/mol), and Ca2⁺ (223 kJ/mol). Understanding the interaction between these metal ions and phosphodiester bonds offers insight into DNA stability and organic data storage systems. METHODS: DFT calculations were employed using Gaussian 16 software, applying the B3LYP hybrid functional with def2-SVP basis sets and GD3BJ dispersion corrections. Full geometry optimizations were performed for the initial and transition states, followed by identifying energy barriers associated with phosphodiester bond cleavage. The optimization criteria included maximum force, root-mean-square force, displacement, and energy convergence thresholds.

Structure and Intercalation of Cysteine-Asparagine-Serine Peptide into Montmorillonite as an Anti-Inflammatory Agent Preparation-A DFT Study.

Peptides are receiving significant attention in pharmaceutical sciences due to their applications as anti-inflammatory drugs; however, many aspects of their interactions and mechanisms at the molecular level are not well-known. This work explores the molecular structure of two peptides-(i) cysteine (Cys)-asparagine (Asn)-serine (Ser) (CNS) as a molecule in the gas phase and solvated in water in zwitterion form, and (ii) the crystal structure of the dipeptide serine-asparagine (SN), a reliable peptide indication whose experimental cell parameters are well known. A search was performed by means of atomistic calculations based on density functional theory (DFT). These calculations matched the experimental crystal structure of SN, validating the CNS results and useful for assignments of our experimental spectroscopic IR bands. Our calculations also explore the intercalation of CNS into the interlayer space of montmorillonite (MNT). Our quantum mechanical calculations show that the conformations of these peptides change significantly during intercalation into the confined interlayer space of MNT. This intercalation is energetically favorable, indicating that this process can be a useful preparation for therapeutic anti-inflammatory applications and showing high stability and controlled release processes.

In Silico Molecular Modeling of Four New Afatinib Derived Molecules Targeting the Inhibition of the Mutated Form of BCR-ABL T315I.

Four afatinib derivatives were designed and modeled. These derivatives were compared to the known tyrosine-kinase inhibitors in treating Chronic Myeloid Leukemia, i.e., imatinib and ponatinib. The molecules were evaluated through computational methods, including docking studies, the non-covalent interaction index, Electron Localization and Fukui Functions, in silico ADMET analysis, QTAIM, and Heat Map analysis. The AFA(IV) candidate significantly increases the score value compared to afatinib. Furthermore, AFA(IV) was shown to be relatively similar to the ponatinib profile when evaluating a range of molecular descriptors. The addition of a methylpiperazine ring seems to be well distributed in the structure of afatinib when targeting the BCR-ABL enzyme, providing an important hydrogen bond interaction with the Asp381 residue of the DFG-switch of BCR-ABL active site residue and the AFA(IV) new chemical entities. Finally, in silico toxicity predictions show a favorable index, with some molecules presenting the loss of the irritant properties associated with afatinib in theoretical predictions.

Construction of amino acids reduced alphabets from molecular descriptors for interpretation of N-carbamylase, luciferase and PI3K mutations.

The classification of amino acids has proven to be a useful tool for understanding the importance of sequence in protein function. The reduced amino acid alphabets are an example of these classifications, which, when built from physicochemical, structural and quantum characteristics of the amino acids, allow it to simplify the representation of the sequences, being useful in the modelling, design and understanding of proteins. So, an objective selection of amino acids properties is important, due classes formed in a reduced alphabet depend on the descriptors used for classification. In this research, based on a careful selection of descriptors for the 20 amino acids, through techniques such as the information content index and hierarchical cluster analysis with ties in proximity, 20,871,586 reduced amino acid alphabets were constructed. This large collection of reduced alphabets was been used to interpret alterations in the function of three proteins: N-carbamylase, Luciferase, and PI3K, caused by amino acid changes in their sequences. For this, the similar and different descriptors linked to these mutations were studied. Properties such as volume, hydrophobicity, charge and autocorrelation can be associated with variations in the behaviour of these proteins, while the frequency in specific secondary structures, the Gibbs free energy and some topological and quantum properties can be considered as the causes of preventing the deactivation of protein function. This work offers the most complete collection of reduced alphabets that promise to be a useful tool for the interpretation of alterations caused by amino acid mutations in the protein sequence.

In silico analysis of molecular mimicry between human aquaporin 3, Aspergillus fumigatus aquaporin and aquaporins from allergic sources.

Background: Atopic dermatitis (AD) is a chronic inflammatory skin condition that has a significant impact on quality of life. The immune response and allergy symptoms in AD are triggered by the recognition of specific allergens by IgE antibodies. Cross-reactivity can lead to auto-IgE responses, potentially worsening AD symptoms. Our research aimed to enhance our understanding of allergenic sources, including A. fumigatus, and their role in AD. We focused on molecular mimicry between human AQP3 and A. fumigatus aquaporin. Methods: In our in-silico analysis, we compared the amino acid sequences of human aquaporin 3 (AQP3) and A. fumigatus aquaporin with 25 aquaporins from various allergenic sources, sourced from the UniProt and NCBI databases. Phylogenetic relationship analysis and homology-based modeling were conducted. We identified conserved antigenic regions located within the 3D structures. Results: The global identity levels among the studied aquaporins averaged 32.6%. One antigenic site exhibited a remarkable local region, with a conserved identity of 71.4%. We categorized the aquaporins into five monophyletic clades (A-E), with group B showing the highest identity (95%), including six mammalian aquaporins, including AQP3. When comparing A. fumigatus aquaporins, the highest identity was observed with Malassezia sympodialis at 35%. Both human and A. fumigatus aquaporins have three linear and three discontinuous epitopes. Conclusions: We identified potential linear and conformational epitopes of AQP3, indicating a possible molecular mimicry between humans and A. fumigatus aquaporins. This suggests autoreactivity and potential cross-reactivity, although further validation using in vitro and in vivo experiments is required.

A contact-based analysis of local energetic frustration dynamics identifies key residues enabling RfaH fold-switch.

Fold-switching enables metamorphic proteins to reversibly interconvert between two highly dissimilar native states to regulate their protein functions. While about 100 proteins have been identified to undergo fold-switching, unveiling the key residues behind this mechanism for each protein remains challenging. Reasoning that fold-switching in proteins is driven by dynamic changes in local energetic frustration, we combined fold-switching simulations generated using simplified structure-based models with frustration analysis to identify key residues involved in this process based on the change in the density of minimally frustrated contacts during refolding. Using this approach to analyze the fold-switch of the bacterial transcription factor RfaH, we identified 20 residues that significantly change their frustration during its fold-switch, some of which have been experimentally and computationally reported in previous works. Our approach, which we developed as an additional module for the FrustratometeR package, highlights the role of local frustration dynamics in protein fold-switching and offers a robust tool to enhance our understanding of other proteins with significant conformational shifts.

Predicting biological activity and design of 5-HT6 antagonists through assessment of ANN-QSAR models in the context of Alzheimer's disease.

CONTEXT: Alzheimer's disease (AD) is the leading cause of dementia around the world, totaling about 55 million cases, with an estimated growth to 74.7 million cases in 2030, which makes its treatment widely desired. Several studies and strategies are being developed considering the main theories regarding its origin since it is not yet fully understood. Among these strategies, the 5-HT6 receptor antagonism emerges as an auspicious and viable symptomatic treatment approach for AD. The 5-HT6 receptor belongs to the G protein-coupled receptor (GPCR) family and is closely implicated in memory loss processes. As a serotonin receptor, it plays an important role in cognitive function. Consequently, targeting this receptor presents a compelling therapeutic opportunity. By employing antagonists to block its activity, the 5-HT6 receptor's functions can be effectively modulated, leading to potential improvements in cognition and memory. METHODS: Addressing this challenge, our research explored a promising avenue in drug discovery for AD, employing Artificial Neural Networks-Quantitative Structure-Activity Relationship (ANN-QSAR) models. These models have demonstrated great potential in predicting the biological activity of compounds based on their molecular structures. By harnessing the capabilities of machine learning and computational chemistry, we aimed to create a systematic approach for analyzing and forecasting the activity of potential drug candidates, thus streamlining the drug discovery process. We assembled a diverse set of compounds targeting this receptor and utilized density functional theory (DFT) calculations to extract essential molecular descriptors, effectively representing the structural features of the compounds. Subsequently, these molecular descriptors served as input for training the ANN-QSAR models alongside corresponding biological activity data, enabling us to predict the potential efficacy of novel compounds as 5-hydroxytryptamine receptor 6 (5-HT6) antagonists. Through extensive analysis and validation of ANN-QSAR models, we identified eight new promising compounds with therapeutic potential against AD.

Optical sensing of L-dihydroxy-phenylalanine in water by a high-affinity molecular receptor involving cooperative binding of a metal coordination bond and boronate-diol.

Selective recognition and sensing of catecholamine-based neurotransmitters by fluorescent synthetic receptors capable of operating in pure water is a central topic of modern supramolecular chemistry that impacts biological and analytical chemistry. Despite advances achieved in the recognition of some neurotransmitters such as dopamine, little effort has been invested in the optical recognition of other neurotransmitters of paramount importance in biochemistry and medicinal chemistry such as the drug L-dihydroxy-phenylalanine (levodopa). Herein, a cationic Cu(II)-terpyridine complex bearing an intramolecular fluorescent quinolinium ring covalently linked to phenylboronic acid (CuL1) was synthesized, structurally described by single-crystal X-ray diffraction and studied in-depth as a fluorescent receptor for neurotransmitters in water. The complex CuL1 was designed to act as a receptor for levodopa through two Lewis acids of different natures (Cu(II) and B atoms) as cooperative binding points. The receptor CuL1 was found to have a strongly acidified -B(OH)2 group (pKa = 6.2) and exceptionally high affinity for levodopa (K = 4.8 × 106 M-1) with selectivity over other related neurotransmitters such as dopamine, epinephrine, norepinephrine and nucleosides in the micromolar concentration range at physiological pH. Such levodopa affinity/selectivity for a boronic acid-based receptor in water is still rare. On the basis of spectroscopic tools (11B NMR, UV-vis, EPR, and fluorescence), high-resolution ESI-MS, crystal structure, and DFT calculations, the interaction mode of CuL1 with levodopa is proposed in a 1 : 1 model using two-point recognition involving a boronate-catechol esterification and a coordination bond Cu(II)-carboxylate. Furthermore, a visual sensing ensemble was constructed using CuL1 and the commercial fluorescent dye eosin Y. Levodopa is efficiently detected by the displacement of the eosin Y bound to the Cu(II)-receptor, monitoring its green emission. The use of Cu(II)-boronate complexes for fast and selective neurotransmitter sensing was unexplored until now.

Exploring the potential of structure-based deep learning approaches for T cell receptor design.

Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optimization of existing ones designed for specific functions, such as binding a target protein. Despite the demonstrated potential of such approaches in designing general protein binders, their application in designing immunotherapeutics remains relatively underexplored. A relevant application is the design of T cell receptors (TCRs). Given the crucial role of T cells in mediating immune responses, redirecting these cells to tumor or infected target cells through the engineering of TCRs has shown promising results in treating diseases, especially cancer. However, the computational design of TCR interactions presents challenges for current physics-based methods, particularly due to the unique natural characteristics of these interfaces, such as low affinity and cross-reactivity. For this reason, in this study, we explored the potential of two structure-based deep learning protein design methods, ProteinMPNN and ESM-IF1, in designing fixed-backbone TCRs for binding target antigenic peptides presented by the MHC through different design scenarios. To evaluate TCR designs, we employed a comprehensive set of sequence- and structure-based metrics, highlighting the benefits of these methods in comparison to classical physics-based design methods and identifying deficiencies for improvement.

The Feline calicivirus Leader of the Capsid (LC) Protein Contains a Putative Transmembrane Domain, Binds to the Cytoplasmic Membrane, and Exogenously Permeates Cells.

Feline calicivirus (FCV), an important model for studying the biology of the Caliciviridae family, encodes the leader of the capsid (LC) protein, a viral factor known to induce apoptosis when expressed in a virus-free system. Our research has shown that the FCV LC protein forms disulfide bond-dependent homo-oligomers and exhibits intrinsic toxicity; however, it lacked a polybasic region and a transmembrane domain (TMD); thus, it was initially classified as a non-classical viroporin. The unique nature of the FCV LC protein, with no similarity to other proteins beyond the Vesivirus genus, has posed challenges for bioinformatic analysis reliant on sequence similarity. In this study, we continued characterizing the LC protein using the AlphaFold 2 and the recently released AlphaFold 3 artificial intelligence tools to predict the LC protein tertiary structure. We compared it to other molecular modeling algorithms, such as I-Tasser's QUARK, offering new insights into its putative TMD. Through exogenous interaction, we found that the recombinant LC protein associates with the CrFK plasmatic membrane and can permeate cell membranes in a disulfide bond-independent manner, suggesting that this interaction might occur through a TMD. Additionally, we examined its potential to activate the intrinsic apoptosis pathway in murine and human ovarian cancer cell lines, overexpressing survivin, an anti-apoptotic protein. All these results enhance our understanding of the LC protein's mechanism of action and suggest its role as a class-I viroporin.

Crystal structure of dihydroneopterin aldolase from Mycobacterium tuberculosis associated with 8-mercaptoguanine, and development of novel S8-functionalized analogues as inhibitors: Synthesis, enzyme inhibition, in vitro toxicity and antitubercular activity.

The crystallographic structure of the FolB enzyme from Mycobacterium tuberculosis (MtFolB), complexed with its inhibitor 8-mercaptoguanine (8-MG), was elucidated at a resolution of 1.95 Å. A novel series of S8-functionalized 8-MG derivatives were synthesised and evaluated as in vitro inhibitors of dihydroneopterin aldolase (DHNA, EC 4.1.2.25) activity of MtFolB. These compounds exhibited IC50 values in the submicromolar range. Evaluation of the activity for five compounds indicated their inhibition mode and inhibition constants. Molecular docking analyses were performed to determine the enzyme-inhibitor intermolecular interactions and ligand conformations upon complex formation. The inhibitory activities of all compounds against the M. tuberculosis H37Rv strain were evaluated. Compound 3e exhibited a minimum inhibitory concentration in the micromolar range. Finally, Compound 3e showed no apparent toxicity in both HepG2 and Vero cells. The findings presented herein will advance the quest for novel, specific inhibitors targeting MtFolB, an attractive molecular target for TB drug development.

Structural Characterization of Heat Shock Protein 90ß and Molecular Interactions with Geldanamycin and Ritonavir: A Computational Study.

Drug repositioning is an important therapeutic strategy for treating breast cancer. Hsp90ß chaperone is an attractive target for inhibiting cell progression. Its structure has a disordered and flexible linker region between the N-terminal and central domains. Geldanamycin was the first Hsp90ß inhibitor to interact specifically at the N-terminal site. Owing to the toxicity of geldanamycin, we investigated the repositioning of ritonavir as an Hsp90ß inhibitor, taking advantage of its proven efficacy against cancer. In this study, we used molecular modeling techniques to analyze the contribution of the Hsp90ß linker region to the flexibility and interaction between the ligands geldanamycin, ritonavir, and Hsp90ß. Our findings indicate that the linker region is responsible for the fluctuation and overall protein motion without disturbing the interaction between the inhibitors and the N-terminus. We also found that ritonavir established similar interactions with the substrate ATP triphosphate, filling the same pharmacophore zone.