Multi-step Computational Approach Research Articles

Sinefungin analogs targeting VP39 methyltransferase as potential anti-monkeypox therapeutics: a multi-step computational approach.

The increasing spread of the Monkeypox virus (MPXV) presents a significant public health challenge, emphasising the urgent requirement for effective treatments. Our study focuses on the VP39 Methyltransferase enzyme of MPXV as a critical target for therapy. By utilising virtual screening, we investigated natural compounds with structural similarities to sinefungin, a broad-acting MTase inhibitor. From an initial set of 177 compounds, we identified three promising compounds-CNP0346326, CNP0343532, and CNP008361, whose binding scores were notably close to that of sinefungin. These candidates bonded strongly to the VP39 enzyme, hinting at a notable potential to impede the virus. Our rigorous computational assays, including re-docking, extended molecular dynamics simulations, and energetics analyses, validate the robustness of these interactions. The data paint a promising picture of these natural compounds as front-runners in the ongoing race to develop MPXV therapeutics and set the stage for subsequent empirical trials to refine these discoveries into actionable medical interventions.

Multimillion Atom Simulations of Di-8-ANEPPS Chromophores Embedded in a Model Plasma Membrane: Toward the Investigation of Realistic Dyed Cell Membranes.

A multistep computational approach has been employed to study a multimillion all-atom dyed plasma membrane, with no less than 42 different lipid species spanning the major head groups and a variety of fatty acids, as well as cholesterol, with the objective of investigating its structure and dynamics, as well as its impact on the embedded di-8-ANEPPS dyes. The latter are commonly used as bioimaging probes and serve as local microscopes. So, they provide information on membrane morphology via their second harmonic nonlinear optical (NLO) responses, which have the advantage of being specific to interface regions and sensitive to the chromophore environment. In previous studies, this chromophore has only been studied in simpler membrane models, far from the complexity of real lipid bilayers, while, owing to the ever-increasing computational resources, multimillion lipid bilayers have been studied, giving access to the effects of its heterogeneity. First, using molecular dynamics (MD) simulations, it is found that the combination of lipids produces a more ordered and denser membrane compared to its homogeneous model counterparts, while the local environment of the embedded dyes becomes enriched in phosphatidylcholine. Subsequently, the second harmonic first hyperpolarizability of the probes was calculated at the TDDFT level on selected frames of MD, highlighting the influence of the lipid environment. Due to the complexity of the system, machine learning (ML) tools have been employed to establish relationships between the membrane structural parameters, the orientation of the probes, and their NLO responses. These ML approaches have revealed influential features, including the presence of diacylglycerol lipids close to the dye. On the whole, this work provides a first step toward understanding the cooperation, synergy, and interactions that occur in such complex guest-host environments, which have emerged as new targets for drug design and membrane lipid therapy.

Identification of immune-related lncRNA in sepsis by construction of ceRNA network and integrating bioinformatic analysis.

Sepsis is a high mortality disease which seriously threatens human life and health, for which the pathogenetic mechanism still unclear. There is increasing evidence showed that immune and inflammation responses are key players in the development of sepsis pathology. LncRNAs, which act as ceRNAs, have critical roles in various diseases. However, the regulatory roles of ceRNA in the immunopathogenesis of sepsis have not yet been elucidated. In this study, we aimed to identify immune biomarkers associated with sepsis. We first generated a global immune-associated ceRNA (IMCE) network based on data describing interactions pairs of gene-miRNA and miRNA-lncRNA. Afterward, we excavated a dysregulated sepsis immune-associated ceRNA (SPIMC) network from the global IMCE network by means of a multi-step computational approach. Functional enrichment indicated that lncRNAs in SPIMC network have pivotal roles in the immune mechanism underlying sepsis. Subsequently, we identified module and hub genes (CD4 and STAT4) via construction of a sepsis immune-related PPI network. Then, we identified hub genes based on the modular structure of PPI network and generated a ceRNA subnetwork to analyze key lncRNAs associated with sepsis. Finally, 6 lncRNAs (LINC00265, LINC00893, NDUFA6-AS1, NOP14-AS1, PRKCQ-AS1 and ZNF674-AS1) that identified as immune biomarkers of sepsis. Moreover, the CIBERSORT algorithm and the infiltration of circulating immune cells types were performed to identify the inflammatory state of sepsis. Correlation analyses between immune cells and sepsis immune biomarkers showed that the LINC00265 was strongly positive correlated with the macrophages M2 (r = 0.77). Collectively, these results may suggest that these lncRNAs (LINC00265, LINC00893, NDUFA6-AS1, NOP14-AS1, PRKCQ-AS1 and ZNF674-AS1) played important roles in the immune pathogenesis of sepsis and provide potential therapeutic targets for further researches on immune therapy treatment in patients with sepsis.

The Computational Preventive Potential of the Rare Flavonoid, Patuletin, Isolated from Tagetes patula, against SARS-CoV-2.

The rare flavonoid, patuletin, was isolated from the flowers of Tagetes patula growing in Egypt. The rarity of the isolated compound inspired us to scrutinize its preventive effect against COVID-19 utilizing a multi-step computational approach. Firstly, a structural similarity study was carried out against nine ligands of nine SARS-CoV-2 proteins. The results showed a large structural similarity between patuletin and F86, the ligand of SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). Then, a 3D-Flexible alignment study of patuletin and F86 verified the proposed similarity. To determine the binding opportunity, patuletin was docked against the RdRp showing a correct binding inside its active pocket with an energy of −20 kcal/mol that was comparable to that of F86 (−23 kcal/mol). Following, several MD simulations as well as MM-PBSA studies authenticated the accurate binding of patuletin in the RdRp via the correct dynamic and energetic behaviors over 100 ns. Additionally, in silico ADMET studies showed the general safety and drug-likeness of patuletin.

Targeting Penicillium expansum GMC Oxidoreductase with High Affinity Small Molecules for Reducing Patulin Production.

Simple SummaryWith the urgent necessity of potential treatments for limiting mycotoxin production and postharvest fungal rots, we propose a combined in silico/in vitro/in vivo strategy for the rapid and effective identification of bioactive small molecules, chosen among a chemical library hosting approved drugs and phytochemicals, to be used after harvest. The molecular target of our analysis was the GMC oxidoreductase from Penicillium expansum involved in the biosynthesis of patulin, a mycotoxin that can contaminate many foods, especially fruits and fruit-based products. The employed in silico/in vitro/in vivo assays described in our study proved the effectiveness of our strategy and in particular of two small molecules, 6-hydroxycoumarin (structurally related to umbelliferon, an already characterized patulin synthase inhibitor) and meticrane (an already approved drug) in reducing patulin accumulation. Our findings highly recommend the mentioned ligands to be subjected to further analysis for being used in the next future in place of other more toxic compounds, in postharvest treatments based on dipping or drenching methods.Flavine adenine dinucleotide (FAD) dependent glucose methanol choline oxidoreductase (GMC oxidoreductase) is the terminal key enzyme of the patulin biosynthetic pathway. GMC oxidoreductase catalyzes the oxidative ring closure of (E)-ascladiol to patulin. Currently, no protein involved in the patulin biosynthesis in Penicillium expansum has been experimentally characterized or solved by X-ray diffraction. Consequently, nothing is known about P. expansum GMC oxidoreductase substrate-binding site and mode of action. In the present investigation, a 3D comparative model for P. expansum GMC oxidoreductase has been described. Furthermore, a multistep computational approach was used to identify P. expansum GMC oxidoreductase residues involved in the FAD binding and in substrate recognition. Notably, the obtained 3D comparative model of P. expansum GMC oxidoreductase was used for performing a virtual screening of a chemical/drug library, which allowed to predict new GMC oxidoreductase high affinity ligands to be tested in in vitro/in vivo assays. In vitro assays performed in presence of 6-hydroxycoumarin and meticrane, among the highly affinity predicted binders, confirmed a dose-dependent inhibition (17–81%) of patulin production by 6-hydroxycoumarin (10 µM–1 mM concentration range), whereas the approved drug meticrane inhibited patulin production by 43% already at 10 µM. Furthermore, 6-hydroxycoumarin and meticrane caused a 60 and 41% reduction of patulin production, respectively, in vivo on apples at 100 µg/wound.

Computer‐aided drug design of small molecule inhibitors of the ERCC1‐XPF protein–protein interaction

The heterodimer of DNA excision repair protein ERCC-1 and DNA repair endonuclease XPF (ERCC1-XPF) is a 5'-3' structure-specific endonuclease essential for the nucleotide excision repair (NER) pathway, and it is also involved in other DNA repair pathways. In cancer cells, ERCC1-XPF plays a central role in repairing DNA damage induced by chemotherapeutics including platinum-based and cross-linking agents; thus, its inhibition is a promising strategy to enhance the effect of these therapies. In this study, we rationally modified the structure of F06, a small molecule inhibitor of the ERCC1-XPF interaction (Molecular Pharmacology, 84, 2013 and 12), to improve its binding to the target. We followed a multi-step computational approach to investigate potential modification sites of F06, rationally design and rank a library of analogues, and identify candidates for chemical synthesis and in vitro testing. Our top compound, B5, showed an improved half-maximum inhibitory concentration (IC50 ) value of 0.49µM for the inhibition of ERCC1-XPF endonuclease activit, and lays the foundation for further testing and optimization. Also, the computational approach reported here can be used to develop DNA repair inhibitors targeting the ERCC1-XPF complex.

Identification of alternative splicing and lncRNA genes in pathogenesis of small cell lung cancer based on their RNA sequencing

The molecular mechanisms involved in small-cell lung cancer (SCLC) are largely unknown. Recent studies have suggested that long non-coding RNAs (lncRNAs) are likely to play a critical role. There is an urgent need for suitable molecular biomarkers for SCLC diagnosis and for assessing patient prognosis. In this study, we used public databases to identify mRNA-like candidate lncRNAs. A multi-step computational approach was used to construct a functional SCLC lncRNAs-mediated competing with endogenous RNA (ceRNA) network (LMCN) by integrating genome-wide lncRNAs and mRNA expression profiles, miRNA-target interactions, functional analyses, and clinical survival analyses. The results revealed the significance of lncRNAs interactions with ceRNAs in SCLC, indicating that integration of expression profiles and alternative splicing could be used to identify biomarkers and the underlying pathological changes. The following genes: EPB41L4A-AS1, HOXA-AS2, XIST, DLEU2, FGD5-AS1, ALMS1-IT1, SNHG12, MIR17HG, MIR4720, and SCARNA10 in cluster, as well as shared alternative splicing events, were considered to be critical genes. Olfactory transduction and endocytosis were the top-enriched pathways in SCLC. The selected cluster, including critical genes, might also be a potential pathway of SCLC pathogenesis. As a result, this research provides the perspective information to explore the potential critical genes and its pathways in SCLC therapy.

Mechanisms of DNA hybridization: Transition path analysis of a simulation-informed Markov model.

Complementary deoxyribonucleic acid (DNA) strands in solution reliably hybridize to form stable duplexes. We study the kinetics of the hybridization process and the mechanisms by which two initially isolated strands come together to form a stable double helix. We adopt a multi-step computational approach. First, we perform a large number of Brownian dynamics simulations of the hybridization process using the coarse-grained oxDNA2 model. Second, we use these simulations to construct a Markov state model of DNA dynamics that uses a state decomposition based on the inter-strand hydrogen bonding pattern. Third, we take advantage of transition path theory to obtain quantitative information about the thermodynamic and dynamic properties of the hybridization process. We find that while there is a large ensemble of possible hybridization pathways, there is a single dominant mechanism in which an initial base pair forms close to either end of the nascent double helix, and the remaining bases pair sequentially in a zipper-like fashion. We also show that the number of formed base pairs by itself is insufficient to describe the transition state of the hybridization process.

Identification of prognostic biomarkers of prostate cancer with long non-coding RNA-mediated competitive endogenous RNA network.

Prostate cancer (PCa) is the second most frequently diagnosed male cancer, and no treatments exist for effective inhibition of metastatic spread of PCa. Long non-coding RNA (lncRNA) plays key roles in pathogenesis and development of various cancers through competing with endogenous RNAs (ceRNAs), but at present research on lncRNA functions in PCa is still very limited. Hence, this aspect was investigated using bioinformatics methods. Firstly, the functional lncRNA-mediated ceRNA network associated with PCa was constructed by the multi-step computational approach. Then the cytoscape software was used to analyze the node degree and betweenness centrality (BC) value of lncRNAs and mRNAs in the interaction. Finally, the lncRNAs were screened in the central region of the network by the node degree and BC value, and the functional enrichment of mRNAs was evaluated with the Gene Ontology (GO) database. In our results, LINC00476, MALAT1, SNHG11, LINC00649, and ILF3-AS1 are the lncRNAs which have the most nodes and higher BC values and considered as prognostic markers in PCa. GO analysis suggested that the function of screened lncRNAs was obviously focused on intracellular receptor signaling pathway, which indicated these lncRNAs might be potential biomarkers for diagnosis, evaluation and gene-targeted therapy of PCa.

Identification of genes and pathways related with cerebral small vessel disease based on a long non-coding RNA-mediated, competitive endogenous RNA network.

This study aimed to investigate the cerebral small vessel disease (SVD), an intrinsic disorder of the brain's perforating arterioles, which could cause serious cognitive decreasing and trigger dementia. In this study, we present a multi-step computational approach to construct a functional SVD long non-coding RNA (lncRNA)-mediated ceRNA network (LMCN) by integrating genome-wide lncRNA and mRNA expression profiles, miRNA-target interactions, and functional analyses. We used hypergeometric test to evaluate enrichment significance of miRNAs and we obtained the LMCN, which contained 27 lncRNAs, 7,229 mRNA, and 28,871 lncRNAs-mRNA interrelationship pairs. What's more, co-expression analysis was utilized to constructe a competitive endogenous RNAs (ceRNAs) interaction network which comprised of 21 lncRNAs, 129 mRNAs and 141 interaction pairs. We determined that metastasis-associated lung adenocarcinoma transcript 1 and MIR155 host gene acted synergistically to regulate mRNAs in a network module of the competitive LMCN. Moreover, 7 genes were obtained through Gene Ontology enrichment. In addition, 29 mRNA enriched pathways significantly associated with lncRNAs was obtained via Fisher test. In conclusion, we identified 7 potential lncRNAs and 29 possible lncRNA-mediated pathways associated with SVD. Thus, ceRNAs could accelerate biomarker discovery and therapeutic development in SVD.

Synthetic B-Cell Epitopes Eliciting Cross-Neutralizing Antibodies: Strategies for Future Dengue Vaccine.

Dengue virus (DENV) is a major public health threat worldwide. A key element in protection from dengue fever is the neutralising antibody response. Anti-dengue IgG purified from DENV-2 infected human sera showed reactivity against several peptides when evaluated by ELISA and epitope extraction techniques. A multi-step computational approach predicted six antigenic regions within the E protein of DENV-2 that concur with the 6 epitopes identified by the combined ELISA and epitope extraction approach. The selected peptides representing B-cell epitopes were attached to a known dengue T-helper epitope and evaluated for their vaccine potency. Immunization of mice revealed two novel synthetic vaccine constructs that elicited good humoral immune responses and produced cross-reactive neutralising antibodies against DENV-1, 2 and 3. The findings indicate new directions for epitope mapping and contribute towards the future development of multi-epitope based synthetic peptide vaccine.

Identification of prognostic biomarkers in glioblastoma using a long non-coding RNA-mediated, competitive endogenous RNA network.

Glioblastoma multiforme (GBM) is a highly malignant brain tumor associated with a poor prognosis. Cross-talk between competitive endogenous RNAs (ceRNAs) plays a critical role in tumor development and physiology. In this study, we present a multi-step computational approach to construct a functional GBM long non-coding RNA (lncRNA)-mediated ceRNA network (LMCN) by integrating genome-wide lncRNA and mRNA expression profiles, miRNA-target interactions, functional analyses, and clinical survival analyses. LncRNAs in the LMCN exhibited specific topological features consistent with a regulatory association with coding mRNAs across GBM pathology. We determined that the lncRNA MCM3AP-AS was involved in RNA processing and cell cycle-related functions, and was correlated with patient survival. MCM3AP-AS and MIR17HG acted synergistically to regulate mRNAs in a network module of the competitive LMCN. By integrating the expression profile of this module into a risk model, we stratified GBM patients in both the The Cancer Genome Atlas and an independent GBM dataset into distinct risk groups. Finally, survival analyses demonstrated that the lncRNAs and network module are potential prognostic biomarkers for GBM. Thus, ceRNAs could accelerate biomarker discovery and therapeutic development in GBM.

Prediction of high- and low-affinity quinol-analogue-binding sites in the aa3 and bo3 terminal oxidases from Bacillus subtilis and Escherichia coli1.

Haem-copper oxidases are the terminal enzymes in both prokaryotic and eukaryotic respiratory chains. They catalyse the reduction of dioxygen to water and convert redox energy into a transmembrane electrochemical proton gradient during their catalytic activity. Haem-copper oxidases show substantial structure similarity, but spectroscopic and biochemical analyses indicate that these enzymes contain diverse prosthetic groups and use different substrates (i.e. cytochrome c or quinol). Owing to difficulties in membrane protein crystallization, there are no definitive structural data about the quinol oxidase physiological substrate-binding site(s). In the present paper, we propose an atomic structure model for the menaquinol:O2 oxidoreductase of Bacillus subtilis (QOx.aa3). Furthermore, a multistep computational approach is used to predict residues involved in the menaquinol/menaquinone binding within B. subtilis QOx.aa3 as well as those involved in quinol/quinone binding within Escherichia coli QOx.bo3. Two specific sequence motifs, R70GGXDX4RXQX3PX3FX[D/N/E/Q]X2HYNE97 and G159GSPX2GWX2Y169 (B. subtilis numbering), were highlighted within QOx from Bacillales. Specific residues within the first and the second sequence motif participate in the high- and low-affinity substrate-binding sites respectively. Using comparative analysis, two analogous motifs, R71GFXDX4RXQX8[Y/F]XPPHHYDQ101 and G163EFX3GWX2Y173 (E. coli numbering) were proposed to be involved in Enterobacteriales/Rhodobacterales/Rhodospirillales QOx high- and low-affinity quinol-derivative-binding sites. Results and models are discussed in the context of the literature.

Viral expression associated with gastrointestinal adenocarcinomas in TCGA high-throughput sequencing data

BackgroundUp to 20% of cancers worldwide are thought to be associated with microbial pathogens, including bacteria and viruses. The widely used methods of viral infection detection are usually limited to a few a priori suspected viruses in one cancer type. To our knowledge, there have not been many broad screening approaches to address this problem more comprehensively.MethodsIn this study, we performed a comprehensive screening for viruses in nine common cancers using a multistep computational approach. Tumor transcriptome and genome sequencing data were available from The Cancer Genome Atlas (TCGA). Nine hundred fifty eight primary tumors in nine common cancers with poor prognosis were screened against a non-redundant database of virus sequences. DNA sequences from normal matched tissue specimens were used as controls to test whether each virus is associated with tumors.ResultsWe identified human papilloma virus type 18 (HPV-18) and four human herpes viruses (HHV) types 4, 5, 6B, and 8, also known as EBV, CMV, roseola virus, and KSHV, in colon, rectal, and stomach adenocarcinomas. In total, 59% of screened gastrointestinal adenocarcinomas (GIA) were positive for at least one virus: 26% for EBV, 21% for CMV, 7% for HHV-6B, and 20% for HPV-18. Over 20% of tumors were co-infected with multiple viruses. Two viruses (EBV and CMV) were statistically significantly associated with colorectal cancers when compared to the matched healthy tissues from the same individuals (p = 0.02 and 0.03, respectively). HPV-18 was not detected in DNA, and thus, no association testing was possible. Nevertheless, HPV-18 expression patterns suggest viral integration in the host genome, consistent with the potentially oncogenic nature of HPV-18 in colorectal adenocarcinomas. The estimated counts of viral copies were below one per cell for all identified viruses and approached the detection limit.ConclusionsOur comprehensive screening for viruses in multiple cancer types using next-generation sequencing data clearly demonstrates the presence of viral sequences in GIA. EBV, CMV, and HPV-18 are potentially causal for GIA, although their oncogenic role is yet to be established.

A multi-step simulation of electron mobility in fluorene–benzothiadiazole conjugated polymer – Case study

Recently, organic solar cells have attracted considerable attention in the field of photovoltaic devices. Charge mobility of conjugated polymer materials is one of the important factors that determine the efficiency of organic solar cells. We investigate charge transport characteristics of conjugated polymers using computational means. We employ a multi-step approach that involves the use of the density functional theory (DFT), semiempirical (ZINDO) and Monte Carlo (MC) theoretical methods within the context of a hopping (Marcus–Hush) model to simulate the electron mobility of poly(9,9′-di-n-octylfluorene-alt-benzothiadiazole) (F8BT). Taking into account the three dimensional (3D) structure of the annealed F8BT as obtained from the X-ray diffraction experiments we illustrate that the multi-step computational approach can give electron mobility values that are in good agreement with the experimental data for the ordered (crystalline) system provided the planes of the side-by-side polymer chains are tilted relative to the bottom plane (which is parallel to the substrate) of the unit cell and the π-chains stacked in a direction perpendicular to the substrate have an “alternating” structure. We also find that the inclusion of an orientational disorder in the crystal, in general, tends to reduce the electron mobility of F8BT. The proposed research is a case study. It illustrates that the use of computations/simulations based on a chemical structure of a monomer and known crystal structure of an organic semiconductor is one possible approach to studying charge mobility in organic polymers.

Charge carrier mobility in conjugated organic polymers – Case studies using multi-step computational approach

Organic solar cells, because of their lightweight, low cost and processing flexibility, are attracting considerable attention in the field of photovoltaic cells. Recently, multiscale computational methods have been proposed to study mobilities of organic conjugated polymers that are used in solar cells. In this study, we use a similar multi-step method to investigate charge transport in ordered and orientationally disordered conjugated fluorene and carbazole based organic polymers. The multi-step approach employed in this work involves the use of the density functional theory (DFT), semiempirical (ZINDO) and Monte Carlo (MC) theoretical methods. We compare the one-dimensional (1D) and the three-dimensional (3D) mobility simulation results with experimental values when possible. We find that the simplified multi-step approach used in ordered systems can predict mobility trends that correlate closely with their inter-molecular distances as determined by X-ray experiments. However, the calculated magnitudes of mobilities are two (or more) orders of magnitude higher than the corresponding experimental values for the pristine films. 3D orientationally disordered simulations give decreased mobilities that are roughly of the same order of magnitude as the experimental values. It is known that, currently, numerous approximations are involved even in the most sophisticated multiscale mobility simulations. Our results suggest that, for the purpose of obtaining expediently reasonable mobility values and trends for many conjugated polymers, a simplified multi-step approach can be adequate.