Current status of advancement in remediation technologies for the toxic metal mercury in the environment: A critical review.

Currently, pollution due to heavy metals, in particular dissolved mercury, is a major concern for society and the environment. This work aims to evaluate the current scenario regarding the removal/elimination of mercury. Mercury removal through adsorption is mainly done through artificial resins and metallic-organic frameworks. In the case of the zinc organic framework, it was able to adsorb Hg2+, reaching an adsorption capacity of 802 mg g-1. As for the Hg(0) the coconut husk was found to have the lowest equilibrium time, 30 min, and the highest adsorption capacity of 956.2 mg g-1. Experimental reports and molecular simulation indicate that the adsorption of mercury and other chemical forms occurs due to electrostatic interactions, ion exchange, precipitation, complexation, chelation, and covalent bonds, according to the material nature. The reported thermodynamic results show that, in most cases, the mercury adsorption has an endothermic nature with enthalpy levels below 40 kJ mol-1. Thermal and chemical regeneration methods lead to a similar number of 5 cycles for different materials. The presence of other ions, in particular cadmium, lead, and copper, generates an antagonistic effect for mercury adsorption. Regarding the other current technologies, it was found that mercury removal is feasible through precipitation, phytoremediation, and marine microalgae; all these methods require constant chemicals or a slow rate of removal according to the conditions. Advanced oxidative processes have noteworthy removal of Hg(0); however, Fenton processes lead to mineralization, which leads to Fe2+ and Fe3+ in solution; sonochemical processes are impossible to scale up at the current technology level; and electrochemical processes consume more energy and require constant changes of the anode and cathode. Overall, it is possible to conclude that the adsorption process remains a more friendly, economical, and greener process in comparison with other processes.

Harnessing the potential of microbial keratinases for bioconversion of keratin waste.

The increasing global consumption of poultry meat has led to the generation of a vast quantity of feather keratin waste daily, posing significant environmental challenges due to improper disposal methods. A growing focus is on utilizing keratinous polymeric waste, amounting to millions of tons annually. Keratins are biochemically rigid, fibrous, recalcitrant, physiologically insoluble, and resistant to most common proteolytic enzymes. Microbial biodegradation of feather keratin provides a viable solution for augmenting feather waste's nutritional value while mitigating environmental contamination. This approach offers an alternative to traditional physical and chemical treatments. This review focuses on the recent findings and work trends in the field of keratin degradation by microorganisms (bacteria, actinomycetes, and fungi) via keratinolytic and proteolytic enzymes, as well as the limitations and challenges encountered due to the low thermal stability of keratinase, and degradation in the complex environmental conditions. Therefore, recent biotechnological interventions such as designing novel keratinase with high keratinolytic activity, thermostability, and binding affinity have been elaborated here. Enhancing protein structural rigidity through critical engineering approaches, such as rational design, has shown promise in improving the thermal stability of proteins. Concurrently, metagenomic annotation offers insights into the genetic foundations of keratin breakdown, primarily predicting metabolic potential and identifying probable keratinases. This may extend the understanding of microbial keratinolytic mechanisms in a complex community, recognizing the significance of synergistic interactions, which could be further utilized in optimizing industrial keratin degradation processes.

Computer modeling of the peculiarities in the interaction of IL-1 with its receptors in schizophrenia.

One of the primary theories regarding the development of schizophrenia revolves around genetics, indicating the involvement of hereditary factors in various processes, including inflammation. Research has demonstrated that inflammatory reactions occurring in microglia can impact the progression of the disease. It has also been established that genetically determined changes in IL-1 can contribute to schizophrenia, thereby confirming the role of the IL-1 gene cluster in disease susceptibility. The aim of this study is a computer-based assessment of the structural interactions of IL-1 proteins with their receptors in schizophrenia. The study utilized the DisGeNET database, enabling the assessment of the reliability of identified IL-1 polymorphisms. Polymorphisms were also sought using NCBI PubMed. The NCBI Protein service was employed to search for and analyze the position of the identified polymorphisms on the chromosome. Structures for modeling were extracted from the Protein Data Bank database. Protein modeling was conducted using the SWISS-MODEL server, and protein interaction modeling was performed using PRISM. Notably, this study represents the first prediction of the interactions of IL-1α, IL-1ß, and IL- 1RA proteins, taking into account the presence of single-nucleotide polymorphisms associated with schizophrenia in the sequence of the corresponding genes. The results indicate that the presence of SNP rs315952 in the IL-1RA protein gene, associated with schizophrenia, may lead to a weakening of the IL-1RA binding to receptors, potentially triggering the initiation of the IL-1 signaling pathway by disrupting or weakening the IL-1RA binding to receptors and facilitating the binding of IL-1 to them. Such alterations could potentially lead to a change in the immune response. The data obtained contribute theoretically to the development of ideas about the molecular mechanisms through which hereditary factors in schizophrenia influence the interactions of proteins of the IL-1 family, which play an important role in the processes of the immune system.

(5S)-5-Benzylswainsonines as potent and selective inhibitors of Golgi α-mannosidase II: synthesis, enzyme evaluation and molecular modelling.

Development of novel anti-cancer therapeutics based on Golgi α-mannosidase II (GMII) inhibition is considerably impeded by an undesired co-inhibition of lysosomal α-mannosidase leading to severe side-effects. In this contribution, we describe a fully stereoselective synthesis of (5S)-5-[4-(halo)benzyl]swainsonines as highly potent and selective inhibitors of GMII. The synthesis starts from a previously reported aldehyde readily available from l-ribose, and the key features include an intramolecular reductive amination with substrate-controlled stereoselectivity and a late-stage derivatisation of the benzyl group via ipso-substitution. These novel swainsonine analogues were found to be nanomolar inhibitors of the Golgi-type α-mannosidase AMAN-2 (Ki = 23-75 nM) with excellent selectivity (selectivity index = 205-870) over the lysosomal-type Jack bean α-mannosidase. Finally, molecular docking and pKa calculations were performed to provide more insight into the structure of the inhibitor:enzyme complexes, and a pair interaction energy analysis (FMO-PIEDA) was carried out to rationalise the observed potency and selectivity of the inhibitors.

Rapid mode switching facilitates the growth of Trichodesmium: A model analysis.

Trichodesmium is one of the dominant dinitrogen (N2) fixers in the ocean, influencing global carbon and nitrogen cycles through biochemical reactions. Although its photosynthetic activity fluctuates rapidly, the physiological or ecological advantage of this fluctuation is unclear. We develop a metabolic model of Trichodesmium that can perform daytime N2 fixation. We examined (1) the effect of the duration of switches between photosynthetic and non-photosynthetic cellular states and (2) the effect of the presence and absence of N2 fixation in photosynthetic states. Results show that a rapid switch between photosynthetic and non-photosynthetic states increases Trichodesmium growth rates by improving metabolic efficiencies due to an improved balance of C and N metabolism. This provides a strategy for previous paradoxical observations that all Trichodesmium cells can contain nitrogenase. This study reveals the importance of fluctuating photosynthetic activity and provides a mechanism for daytime N2 fixation that allows Trichodesmium to fix N2 aerobically in the global ocean.

Community metabolic modeling of host-microbiota interactions through multi-objective optimization.

The human gut microbiota comprises various microorganisms engaged in intricate interactions among themselves and with the host, affecting its health. While advancements in omics technologies have led to the inference of clear associations between microbiome composition and health conditions, we usually lack a causal and mechanistic understanding of these associations. For modeling mechanisms driving the interactions, we simulated the organism's metabolism using in silico genome-scale metabolic models (GEMs). We used multi-objective optimization to predict and explain metabolic interactions among gut microbes and an intestinal epithelial cell. We developed a score integrating model simulation results to predict the type (competition, neutralism, mutualism) and quantify the interaction between several organisms. This framework uncovered a potential cross-feeding for choline, explaining the predicted mutualism between Lactobacillus rhamnosus GG and the epithelial cell. Finally, we analyzed a five-organism ecosystem, revealing that a minimal microbiota can favor the epithelial cell's maintenance.

Neuroprotective Efficacy of the Glucocorticoid Receptor Modulator PT150 in the Rotenone Mouse Model of Parkinson's Disease.

Parkinson's disease (PD) is the most common neurodegenerative movement disorder worldwide. Current treatments for PD largely center around dopamine replacement therapies and fail to prevent the progression of pathology, underscoring the need for neuroprotective interventions. Approaches that target neuroinflammation, which occurs prior to dopaminergic neuron (DAn) loss in the substantia nigra (SN), represent a promising therapeutic strategy. The glucocorticoid receptor (GR) has been implicated in the neuropathology of PD and modulates numerous neuroinflammatory signaling pathways in the brain. Therefore, we investigated the neuroprotective effects of the novel GR modulator, PT150, in the rotenone mouse model of PD, postulating that inhibition of glial inflammation would protect DAn and reduce accumulation of neurotoxic misfolded ⍺-synuclein protein. C57Bl/6 mice were exposed to 2.5mg/kg/day rotenone by intraperitoneal injection for 14 days. Upon completion of rotenone dosing, mice were orally treated at day 15 with 30mg/kg/day or 100mg/kg/day PT150 in the 14-day post-lesioning incubation period, during which the majority of DAn loss and α-synuclein (α-syn) accumulation occurs. Our results indicate that treatment with PT150 reduced both loss of DAn and microgliosis in the nigrostriatal pathway. Although morphologic features of astrogliosis were not attenuated, PT150 treatment promoted potentially neuroprotective activity in these cells, including increased phagocytosis of hyperphosphorylated α-syn. Ultimately, PT150 treatment reduced the loss of DAn cell bodies in the SN, but not the striatum, and prohibited intra-neuronal accumulation of α-syn. Together, these data indicate that PT150 effectively reduced SN pathology in the rotenone mouse model of PD.

Computer-aided drug design supporting sunscreen research: a showcase study using previously synthesized hybrid UV filter-antioxidant compounds.

CONTEXT: Although quantum mechanical calculations have proven effective in accurately predicting UV absorption and assessing the antioxidant potential of compounds, the utilization of computer-aided drug design (CADD) to support sustainable synthesis research of new sunscreen active ingredients remains an area with limited exploration. Furthermore, there are ongoing concerns about the safety and effectiveness of existing sunscreens. Therefore, it remains crucial to investigate photoprotection mechanisms and develop enhanced strategies for mitigating the harmful effects of UVR exposure, improving both the safety and efficacy of sunscreen products. A previous study conducted synthesis research on eight novel hybrid compounds (I-VIII) for use in sunscreen products by molecular hybridization of trans-resveratrol (RESV), avobenzone (AVO), and octinoxate (OMC). Herein, time-dependent density functional theory (TD-DFT) calculations performed in the gas phase on the isolated hybrid compounds (I-VIII) proved to reproduce the experimental UV absorption. Resveratrol-avobenzone structure-based hybrids (I-IV) present absorption maxima in the UVB range with slight differences between them, while resveratrol-OMC structure-based hybrids (V-VIII) showed main absorption in the UVA range. Among RESV-OMC hybrids, compounds V and VI exhibited higher UV absorption intensity, and compound VIII stood out for its broad-spectrum coverage in our simulations. Furthermore, both in silico and in vitro analyses revealed that compounds VII and VIII exhibited the highest antioxidant activity, with compound I emerging as the most reactive antioxidant within RESV-AVO hybrids. The study suggests a preference for the hydrogen atom transfer (HAT) mechanism over single-electron transfer followed by proton transfer (SET-PT) in the gas phase. With a strong focus on sustainability, this approach reduces costs and minimizes effluent production in synthesis research, promoting the eco-friendly development of new sunscreen active ingredients. METHODS: The SPARTAN'20 program was utilized for the geometry optimization and energy calculations of all compounds. Conformer distribution analysis was performed using the Merck molecular force field 94 (MMFF94), and geometry optimization was carried out using the parametric method 6 (PM6) followed by density functional theory (DFT/B3LYP/6-31G(d)). The antioxidant behavior of the hybrid compounds (I-VIII) was determined using the highest occupied molecular orbital (εHOMO) and the lowest unoccupied molecular orbital (εLUMO) energies, as well as the bond dissociation enthalpy (BDE), ionization potential (IP), and proton dissociation enthalpy (PDE) values, all calculated at the same level of structural optimization. TD-DFT study is carried out to calculate the excitation energy using the B3LYP functional with the 6-31G(d) basis set. The calculated transitions were convoluted with a Gaussian profile using the Gabedit program.

Molecular self-assembled helix peptide nanotubes based on some amino acid molecules and their dipeptides: molecular modeling studies.

CONTEXT: The paper considers the features of the structure and dipole moments of several amino acids and their dipeptides which play an important role in the formation of the peptide nanotubes based on them. The influence of the features of their chirality (left L and right D) and the alpha-helix conformations of amino acids are taken into account. In particular, amino acids with aromatic rings, such as phenylalanine (Phe/F), and branched-chain amino acids (BCAAs)-leucine (Leu/L) and isoleucine (Ile/I)-as well as corresponding dipeptides (diphenylalanine (FF), dileucine (LL), and diisoleucine (II)) are considered. The main features and properties of these dipeptide structures and peptide nanotubes (PNTs), based on them, are investigated using computational molecular modeling and quantum-chemical semi-empirical calculations. Their polar, piezoelectric, and photoelectronic properties and features are studied in detail. The results of calculations of dipole moments and polarization, as well as piezoelectric coefficients and band gap width, for different types of helical peptide nanotubes are presented. The calculated values of the chirality indices of various nanotubes are given, depending on the chirality of the initial dipeptides-the results obtained are consistent with the law of changes in the type of chirality as the hierarchy of molecular structures becomes more complex. The influence of water molecules in the internal cavity of nanotubes on their physical properties is estimated. A comparison of the results of these calculations by various computational methods with the available experimental data is presented and discussed. METHOD: The main tool for molecular modeling of all studied nanostructures in this work was the HyperChem 8.01 software package. The main approach used here is the Hartree-Fock (HF) self-consistent field (SCF) with various quantum-chemical semi-empirical methods (AM1, PM3, RM1) in the restricted Hartree-Fock (RHF) and in the unrestricted Hartree-Fock (UHF) approximations. Optimization of molecular systems and the search for their optimal geometry is carried out in this work using the Polak-Ribeire algorithm (conjugate gradient method), which determines the optimized geometry at the point of their minimum total energy. For such optimized structures, dipole moments D and electronic energy levels (such as EHOMO and ELUMO), as well as the band gap Eg = ELUMO - EHOMO, were then calculated. For each optimized molecular structure, the volume was calculated using the QSAR program implemented also in the HyperChem software package.

Formulation of next-generation polyvalent vaccine candidates against three important poxviruses by targeting DNA-dependent RNA polymerase using an integrated immunoinformatics and molecular modeling approach.

BACKGROUND: Poxviruses comprise a group of large double-stranded DNA viruses and are known to cause diseases in humans, livestock animals, and other animal species. The Mpox virus (MPXV; formerly Monkeypox), variola virus (VARV), and volepox virus (VPXV) are among the prevalent poxviruses of the Orthopoxviridae genera. The ongoing Mpox infectious disease pandemic caused by the Mpox virus has had a major impact on public health across the globe. To date, only limited repurposed antivirals and vaccines are available for the effective treatment of Mpox and other poxviruses that cause contagious diseases. METHODS: The present study was conducted with the primary goal of formulating multi-epitope vaccines against three evolutionary closed poxviruses i.e., MPXV, VARV, and VPXV using an integrated immunoinformatics and molecular modeling approach. DNA-dependent RNA polymerase (DdRp), a potential vaccine target of poxviruses, has been used to determine immunodominant B and T-cell epitopes followed by interactions analysis with Toll-like receptor 2 at the atomic level. RESULTS: Three multi-epitope vaccine constructs, namely DdRp_MPXV (V1), DdRp_VARV (V2), and DdRp_VPXV (V3) were designed. These vaccine constructs were found to be antigenic, non-allergenic, non-toxic, and soluble with desired physicochemical properties. Protein-protein docking and interaction profiling analysis depicts a strong binding pattern between the targeted immune receptor TLR2 and the structural models of the designed vaccine constructs, and manifested a number of biochemical bonds (hydrogen bonds, salt bridges, and non-bonded contacts). State-of-the-art all-atoms molecular dynamics simulations revealed highly stable interactions of vaccine constructs with TLR2 at the atomic level throughout the simulations on 300 nanoseconds. Additionally, the outcome of the immune simulation analysis suggested that designed vaccines have the potential to induce protective immunity against targeted poxviruses. CONCLUSIONS: Taken together, formulated next-generation polyvalent vaccines were found to have good efficacy against closely related poxviruses (MPXV, VARV, and VPXV) as demonstrated by our extensive immunoinformatics and molecular modeling evaluations; however, further experimental investigations are still needed.

Comparative Binding Study of Gliptins to Bacterial DPP4-like Enzymes for the Treatment of Type 2 Diabetes Mellitus (T2DM).

The role of the gut microbiota and its interplay with host metabolic health, particularly in the context of type 2 diabetes mellitus (T2DM) management, is garnering increasing attention. Dipeptidyl peptidase 4 (DPP4) inhibitors, commonly known as gliptins, constitute a class of drugs extensively used in T2DM treatment. However, their potential interactions with gut microbiota remain poorly understood. In this study, we employed computational methodologies to investigate the binding affinities of various gliptins to DPP4-like homologs produced by intestinal bacteria. The 3D structures of DPP4 homologs from gut microbiota species, including Segatella copri, Phocaeicola vulgatus, Bacteroides uniformis, Parabacteroides merdae, and Alistipes sp., were predicted using computational modeling techniques. Subsequently, molecular dynamics simulations were conducted for 200 ns to ensure the stability of the predicted structures. Stable structures were then utilized to predict the binding interactions with known gliptins through molecular docking algorithms. Our results revealed binding similarities of gliptins toward bacterial DPP4 homologs compared to human DPP4. Specifically, certain gliptins exhibited similar binding scores to bacterial DPP4 homologs as they did with human DPP4, suggesting a potential interaction of these drugs with gut microbiota. These findings could help in understanding the interplay between gliptins and gut microbiota DPP4 homologs, considering the intricate relationship between the host metabolism and microbial communities in the gut.

Molecular and Evolution In Silico Studies Unlock the h4-HPPD C-Terminal Tail Gating Mechanism.

The enzyme 4-hydroxyphenylpyruvate dioxygenase (4-HPPD) is involved in the catabolism of the amino acid tyrosine in organisms such as bacteria, plants, and animals. It catalyzes the conversion of 4-hydroxyphenylpyruvate to a homogenisate in the presence of molecular oxygen and Fe(II) as a cofactor. This enzyme represents a key step in the biosynthesis of important compounds, and its activity deficiency leads to severe, rare autosomal recessive disorders, like tyrosinemia type III and hawkinsinuria, for which no cure is currently available. The 4-HPPD C-terminal tail plays a crucial role in the enzyme catalysis/gating mechanism, ensuring the integrity of the active site for catalysis through fine regulation of the C-terminal tail conformation. However, despite growing interest in the 4-HPPD catalytic mechanism and structure, the gating mechanism remains unclear. Furthermore, the absence of the whole 3D structure makes the bioinformatic approach the only possible study to define the enzyme structure/molecular mechanism. Here, wild-type 4-HPPD and its mutants were deeply dissected by applying a comprehensive bioinformatics/evolution study, and we showed for the first time the entire molecular mechanism and regulation of the enzyme gating process, proposing the full-length 3D structure of human 4-HPPD and two novel key residues involved in the 4-HPPD C-terminal tail conformational change.

Synthesis and anti-SARS-CoV-2 activity of amino acid modified cephalotaxine derivatives.

The severe acute respiratory syndrome coronavirus (SARS-CoV-2) pandemic has triggered a significant impact on global public health security, it is urgent to develop effective antiviral drugs. Previous studies have found that binding to ACE2 is a key step in the invasion of SARS-CoV-2 into host cells, so virus invasion can be inhibited by blocking ACE2, but there are few reports on this kind of specific inhibitor. Our previous study found that Harringtonine (HT) can inhibit the entry of SARS-CoV-2 spike pseudovirus into ACE2h cells, but its relatively high cytotoxicity limits its further development. Amino acid modification of the active components can increase their solubility and reduce their cytotoxicity. Therefore, in this study, seven new derivatives were synthesized by amino acid modification of its core structure Cephalotaxine. The target compounds were evaluated by cell viability assay and the SARS-CoV-2 spike pseudovirus entry assay. Compound CET-1 significantly inhibited the entry of pseudovirus into ACE2h cells and showed less cytotoxicity than HT. Molecular docking results showed that CET-1 could bind TYR83, an important residue of ACE2, just like HT. In conclusion, our study provided a novel compound with more potential activity and lower toxicity than HT on inhibiting the SARS-CoV-2 spike pseudovirus infection, which makes it possible to be a lead compound as an antiviral drug in the future.

Current state of implementation of in silico tools in the biopharmaceutical industry-Proceedings of the 5th modeling workshop.

The fifth modeling workshop (5MW) was held in June 2023 at Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multiphase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized datasets. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyzes without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high-level guidance in ICH Q8-Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and artificial intelligence (AI). Another outcome was alignment on a key definition for "mechanistic modeling." Feedback from participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g., biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter-disciplinary subject matter experts (e.g., data science, chromatography) is essential to advancing the modeling community.

Quinolinyl-based multitarget-directed ligands with soluble epoxide hydrolase and fatty acid amide hydrolase inhibitory activities: Synthetic studies and pharmacological evaluations.

Simultaneous inhibition of soluble epoxide hydrolase (sEH) and fatty acid amide hydrolase (FAAH) with a single small molecule represents a novel therapeutic approach in treating inflammatory pain, since both targets are involved in pain and inflammation processes. In this study using multi-target directed ligands methodology we designed and synthesized 7 quinolinyl-based dual sEH/FAAH inhibitors, using an optimized microwave-assisted Suzuki-Miyaura coupling reaction and tested their potency in human FAAH and human, rat, and mouse sEH inhibition assays. The structure-activity relationship study showed that quinolinyl moiety is well tolerated in the active sites of both enzymes, yielding several very potent dual sEH/FAAH inhibitors with the IC50 values in the low nanomolar range. The most potent dual inhibitor 4d was further evaluated in stability assay in human and rat plasma where it performed better than the standard Warfarin while in vivo study revealed that 1 mg/kg 4d can inhibit acute inflammatory pain in male rats to a similar degree as the traditional nonsteroidal anti-inflammatory drug ketoprofen (30 mg/kg) after intraperitoneal injection. ADMET prediction studies for this dual inhibitor show favorable pharmacokinetic properties which will guide the future in vivo evaluations.

From proteome to candidate vaccines: target discovery and molecular dynamics-guided multi-epitope vaccine engineering against kissing bug.

Introduction: Trypanosoma cruzi is a protozoan parasite that causes the tropical ailment known as Chagas disease, which has its origins in South America. Globally, it has a major impact on health and is transported by insect vector that serves as a parasite. Given the scarcity of vaccines and the limited treatment choices, we conducted a comprehensive investigation of core proteomics to explore a potential reverse vaccine candidate with high antigenicity. Methods: To identify the immunodominant epitopes, T. cruzi core proteomics was initially explored. Consequently, the vaccine sequence was engineered to possess characteristics of non-allergenicity, antigenicity, immunogenicity, and enhanced solubility. After modeling the tertiary structure of the human TLR4 receptor, the binding affinities were assessed employing molecular docking and molecular dynamics simulations (MDS). Results: Docking of the final vaccine design with TLR4 receptors revealed substantial hydrogen bond interactions. A server-based methodology for immunological simulation was developed to forecast the effectiveness against antibodies (IgM + IgG) and interferons (IFN-g). The MDS analysis revealed notable levels of structural compactness and binding stability with average RMSD of 5.03 Aring;, beta-factor 1.09e+5 Å, Rg is 44.7 Aring; and RMSF of 49.50 Aring;. This is followed by binding free energies calculation. The system stability was compromised by the complexes, as evidenced by their corresponding Gibbs free energies of -54.6 kcal/mol. Discussion: Subtractive proteomics approach was applied to determine the antigenic regions of the T cruzi. Our study utilized computational techniques to identify B- and T-cell epitopes in the T. cruzi core proteome. In current study the developed vaccine candidate exhibits immunodominant features. Our findings suggest that formulating a vaccine targeting the causative agent of Chagas disease should be the initial step in its development.

Methylation-Based Characterization of a New IDH2 Mutation in Sinonasal Undifferentiated Carcinoma.

Mutations affecting codon 172 of the isocitrate dehydrogenase 2 (IDH2) gene define a subgroup of sinonasal undifferentiated carcinomas (SNUCs) with a relatively favorable prognosis and a globally hypermethylated phenotype. They are also recurrent (along with IDH1 mutations) in gliomas, acute myeloid leukemia, and intrahepatic cholangiocarcinoma. Commonly reported mutations, all associated with aberrant IDH2 enzymatic activity, include R172K, R172S, R172T, R172G, and R172M. We present a case of SNUC with a never-before-described IDH2 mutation, R172A. Our report compares the methylation pattern of our sample to other cases from the Gene Expression Omnibus database. Hierarchical clustering suggests a strong association between our sample and other IDH-mutant SNUCs and a clear distinction between sinonasal normal tissues and tumors. Principal component analysis (PCA), using 100 principal components explaining 94.5% of the variance, showed the position of our sample to be within 1.02 standard deviation of the other IDH-mutant SNUCs. A molecular modeling analysis of the IDH2 R172A versus other R172 variants provides a structural explanation to how they affect the protein active site. Our findings thus suggest that the R172A mutation in IDH2 confers a gain of function similar to other R172 mutations in IDH2, resulting in a similar hypermethylated profile.

Application of Funnel Metadynamics to the Platelet Integrin αIIbß3 in Complex with an RGD Peptide.

Integrin αIIbß3 mediates platelet aggregation by binding the Arginyl-Glycyl-Aspartic acid (RGD) sequence of fibrinogen. RGD binding occurs at a site topographically proximal to the αIIb and ß3 subunits, promoting the conformational activation of the receptor from bent to extended states. While several experimental approaches have characterized RGD binding to αIIbß3 integrin, applying computational methods has been significantly more challenging due to limited sampling and the need for a priori information regarding the interactions between the RGD peptide and integrin. In this study, we employed all-atom simulations using funnel metadynamics (FM) to evaluate the interactions of an RGD peptide with the αIIb and ß3 subunits of integrin. FM incorporates an external history-dependent potential on selected degrees of freedom while applying a funnel-shaped restraint potential to limit RGD exploration of the unbound state. Furthermore, it does not require a priori information about the interactions, enhancing the sampling at a low computational cost. Our FM simulations reveal significant molecular changes in the ß3 subunit of integrin upon RGD binding and provide a free-energy landscape with a low-energy binding mode surrounded by higher-energy prebinding states. The strong agreement between previous experimental and computational data and our results highlights the reliability of FM as a method for studying dynamic interactions of complex systems such as integrin.

A Practical Guide to All-Atom and Coarse-Grained Molecular Dynamics Simulations Using Amber and Gromacs: A Case Study of Disulfide-Bond Impact on the Intrinsically Disordered Amyloid Beta.

Intrinsically disordered proteins (IDPs) pose challenges to conventional experimental techniques due to their large-scale conformational fluctuations and transient structural elements. This work presents computational methods for studying IDPs at various resolutions using the Amber and Gromacs packages with both all-atom (Amber ff19SB with the OPC water model) and coarse-grained (Martini 3 and SIRAH) approaches. The effectiveness of these methodologies is demonstrated by examining the monomeric form of amyloid-ß (Aß42), an IDP, with and without disulfide bonds at different resolutions. Our results clearly show that the addition of a disulfide bond decreases the ß-content of Aß42; however, it increases the tendency of the monomeric Aß42 to form fibril-like conformations, explaining the various aggregation rates observed in experiments. Moreover, analysis of the monomeric Aß42 compactness, secondary structure content, and comparison between calculated and experimental chemical shifts demonstrates that all three methods provide a reasonable choice to study IDPs; however, coarse-grained approaches may lack some atomistic details, such as secondary structure recognition, due to the simplifications used. In general, this study not only explains the role of disulfide bonds in Aß42 but also provides a step-by-step protocol for setting up, conducting, and analyzing molecular dynamics (MD) simulations, which is adaptable for studying other biomacromolecules, including folded and disordered proteins and peptides.

Molecular modeling of multi-target analogs of huperzine A and applications in Alzheimer's disease.

CONTEXT: Given the diverse pathophysiological mechanisms underlying Alzheimer's disease, it is improbable that a single targeted drug will prove successful as a therapeutic strategy. Therefore, exploring various hypotheses in drug design is imperative. The sequestration of Fe(II) and Zn(II) cations stands out as a crucial mechanism based on the mitigation of reactive oxygen species. Moreover, inhibiting acetylcholinesterase represents a pivotal strategy to enhance acetylcholine levels in the synaptic cleft. This research aims to investigate the analogs of Huperzine A, documented in scientific literature, considering of these two hypotheses. Consequently, the speciation chemistry of these structures with Fe(II) and Zn(II) was scrutinized using quantum chemistry calculations, molecular docking simulations, and theoretical predictions of pharmacokinetics properties. From the pharmacokinetic properties, only two analogs, HupA-A1 and HupA-A2, exhibited a theoretical permeability across the blood-brain barrier; on the other hand, from a thermodynamic standpoint, the enantiomers of HupA-A2 showed negligible chelation values. The enantiomers with the most favorable interaction parameters were S'R'HupA-A1 (ΔGBIND = -40.0 kcal mol-1, fitness score = 35.5) and R'R'HupA-A1 (ΔGBIND = -35.5 kcal mol-1, fitness score = 22.61), being compared with HupA (ΔGBIND = -41.75 kcal mol-1, fitness score = 39.95). From this study, some prime candidates for promising drug were S'R'HupA-A1 and R'R'HupA-A1, primarily owing to their favorable thermodynamic chelating capability and potential anticholinesterase mechanism. METHODS: Quantum chemistry calculations were carried out at B3LYP/6-31G(d) level, considering the IEF-PCM(UFF) implicit solvent model for water. The coordination compounds were assessed using the Gibbs free energy variation and hard and soft acid theory. Molecular docking calculations were conducted using the GOLD program, based on the crystal structure of the acetylcholinesterase protein (PDB code = 4EY5), where the ChemScore function was employed with the active site defined as the region within a 15-Å radius around the centroid coordinates (X = -9.557583, Y = -43.910473, Z = 31.466687). Pharmacokinetic properties were predicted using SwissADME, focusing on Lipinski's rule of five.