A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcome.

Copper is a vital micronutrient involved in many biological processes and is an essential component of tumour cell growth and migration. Copper influences tumour growth through a process called cuproplasia, defined as abnormal copper-dependent cell-growth and proliferation. Copper-chelation therapy targeting this process has demonstrated efficacy in several clinical trials against cancer. While the molecular pathways associated with cuproplasia are partially known, genetic heterogeneity across different cancer types has limited the understanding of how cuproplasia impacts patient survival. Utilising RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks to identify the critical cuproplasia-related genes across 23 different cancer types. From this, we identified a novel 8-gene cuproplasia-related gene signature associated with pan-cancer survival, and a 6-gene prognostic risk score model in low grade glioma. These findings highlight the use of gene regulatory networks to identify cuproplasia-related gene signatures that could be used to generate risk score models. This can potentially identify patients who could benefit from copper-chelation therapy and identifies novel targeted therapeutic strategies.

Stability and cavitation of nanobubble: Insights from large-scale atomistic molecular dynamics simulations.

We perform large-scale atomistic simulations of a system containing 12 × 106 atoms, comprising an oxygen gas-filled bubble immersed in water, to understand the stability and cavitation induced by ultrasound. First, we propose a method to construct a bubble/water system. For a given bubble radius, the pressure inside the bubble is estimated using the Young-Laplace equation. Then, this pressure is used as a reference for a constant temperature, constant pressure simulation of an oxygen system, enabling us to extract a sphere of oxygen gas and place it into a cavity within an equilibrated water box. This ensures that the Young-Laplace equation is satisfied and the bubble is stable in water. Second, this stable bubble is used for ultrasound-induced cavitation simulations. We demonstrate that under weak ultrasound excitation, the bubble undergoes stable cavitation, revealing various fluid velocity patterns, including the first-order velocity field and microstreaming. These fluid patterns emerge around the bubble on a nanometer scale within a few nanoseconds, a phenomenon challenging to observe experimentally. With stronger ultrasound intensities, the bubble expands significantly and then collapses violently. The gas core of the collapsed bubble, measuring 3-4 nm, exhibits starfish shapes with temperatures around 1500 K and pressures around 6000 bar. The simulation results are compared with those from Rayleigh-Plesset equation modeling, showing good agreement. Our simulations provide insights into the stability and cavitation of nanosized bubbles.

A decrease in serum dihydrotestosterone levels in 9-year-old Vietnamese children from a dioxin exposure area.

BACKGROUND: Dioxin is an environmental pollutant as well as an endocrine disruptor in humans. Our longitudinal study wants to clarify the relationship between dioxin exposure and endocrine disorders in children living in the Vietnamese dioxin hotspot. METHOD: Seventeen congeners of polychlorinated dibenzo-p-dioxins/polychlorinated dibenzo-furans (PCDDs/PCDFs) in maternal breast milk and seven serum steroid hormones in children of 43 and 46 mothers and their 9-year-old children from the non-exposure and the hotspot areas were measured, respectively. The steroid metabolic enzyme ratios were calculated based on the hormone level ratio. RESULTS: Most dioxin/furan congeners and toxic equivalents (TEQs) levels were significantly higher in the hotspot than in the non-exposure area, except for 2,4,7,8-TeCDF. The height and weight of girls from the hotspot area were substantially lower and inversely correlated with dioxin congener levels/total TEQs level dioxin. The dihydrotestosterone (DHT) levels in the hotspot were markedly lower than those in non-exposed in both genders. The cortisol concentrations were significantly higher in the hotspot than those from the non-exposure area only in the girls. The DHT/testosterone ratios that exhibited the 5α- or 5ß-reductase activity declined by 50% in the hotspot area for both genders. The DHT levels showed strong inverse correlations with almost the PCDDs/PCDFs congeners and total TEQs dioxin in breast milk. CONCLUSIONS: This finding suggests that dioxin exposure in maternal breast milk might impact children's endocrine system until 9 years old, especially on the DHT biosynthesis.

Development and test of highly accurate endpoint free energy methods. 2: Prediction of logarithm of n-octanol-water partition coefficient (logP) for druglike molecules using MM-PBSA method.

The logarithm of n-octanol-water partition coefficient (logP) is frequently used as an indicator of lipophilicity in drug discovery, which has substantial impacts on the absorption, distribution, metabolism, excretion, and toxicity of a drug candidate. Considering that the experimental measurement of the property is costly and time-consuming, it is of great importance to develop reliable prediction models for logP. In this study, we developed a transfer free energy-based logP prediction model-FElogP. FElogP is based on the simple principle that logP is determined by the free energy change of transferring a molecule from water to n-octanol. The underlying physical method to calculate transfer free energy is the molecular mechanics-Poisson Boltzmann surface area (MM-PBSA), thus this method is named as free energy-based logP (FElogP). The superiority of FElogP model was validated by a large set of 707 structurally diverse molecules in the ZINC database for which the measurement was of high quality. Encouragingly, FElogP outperformed several commonly-used QSPR or machine learning-based logP models, as well as some continuum solvation model-based methods. The root-mean-square error (RMSE) and Pearson correlation coefficient (R) between the predicted and measured values are 0.91 log units and 0.71, respectively, while the runner-up, the logP model implemented in OpenBabel had an RMSE of 1.13 log units and R of 0.67. Given the fact that FElogP was not parameterized against experimental logP directly, its excellent performance is likely to be expanded to arbitrary organic molecules covered by the general AMBER force fields.

In silico binding profile characterization of SARS-CoV-2 spike protein and its mutants bound to human ACE2 receptor.

Severe acute respiratory syndrome coronavirus (SARS-CoV-2), a novel coronavirus, has brought an unprecedented pandemic to the world and affected over 64 million people. The virus infects human using its spike glycoprotein mediated by a crucial area, receptor-binding domain (RBD), to bind to the human ACE2 (hACE2) receptor. Mutations on RBD have been observed in different countries and classified into nine types: A435S, D364Y, G476S, N354D/D364Y, R408I, V341I, V367F, V483A and W436R. Employing molecular dynamics (MD) simulation, we investigated dynamics and structures of the complexes of the prototype and mutant types of SARS-CoV-2 spike RBDs and hACE2. We then probed binding free energies of the prototype and mutant types of RBD with hACE2 protein by using an end-point molecular mechanics Poisson Boltzmann surface area (MM-PBSA) method. According to the result of MM-PBSA binding free energy calculations, we found that V367F and N354D/D364Y mutant types showed enhanced binding affinities with hACE2 compared to the prototype. Our computational protocols were validated by the successful prediction of relative binding free energies between prototype and three mutants: N354D/D364Y, V367F and W436R. Thus, this study provides a reliable computational protocol to fast assess the existing and emerging RBD mutations. More importantly, the binding hotspots identified by using the molecular mechanics generalized Born surface area (MM-GBSA) free energy decomposition approach can guide the rational design of small molecule drugs or vaccines free of drug resistance, to interfere with or eradicate spike-hACE2 binding.

Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction.

Structure-based virtual screenings (SBVSs) play an important role in drug discovery projects. However, it is still a challenge to accurately predict the binding affinity of an arbitrary molecule binds to a drug target and prioritize top ligands from an SBVS. In this study, we developed a novel method, using ligand-residue interaction profiles (IPs) to construct machine learning (ML)-based prediction models, to significantly improve the screening performance in SBVSs. Such a kind of the prediction model is called an IP scoring function (IP-SF). We systematically investigated how to improve the performance of IP-SFs from many perspectives, including the sampling methods before interaction energy calculation and different ML algorithms. Using six drug targets with each having hundreds of known ligands, we conducted a critical evaluation on the developed IP-SFs. The IP-SFs employing a gradient boosting decision tree (GBDT) algorithm in conjunction with the MIN + GB simulation protocol achieved the best overall performance. Its scoring power, ranking power and screening power significantly outperformed the Glide SF. First, compared with Glide, the average values of mean absolute error and root mean square error of GBDT/MIN + GB decreased about 38 and 36%, respectively. Second, the mean values of squared correlation coefficient and predictive index increased about 225 and 73%, respectively. Third, more encouragingly, the average value of the areas under the curve of receiver operating characteristic for six targets by GBDT, 0.87, is significantly better than that by Glide, which is only 0.71. Thus, we expected IP-SFs to have broad and promising applications in SBVSs.

Phosphorylation at Ser289 Enhances the Oligomerization of Tau Repeat R2.

In tauopathies such as Alzheimer's disease (AD), aberrant phosphorylation causes the dissociation of tau proteins from microtubules. The dissociated tau then aggregates into sequent forms from soluble oligomers to paired helical filaments and insoluble neurofibrillary tangles (NFTs). NFTs is a hallmark of AD, while oligomers are found to be the most toxic form of the tau aggregates. Therefore, understanding tau oligomerization with regard to abnormal phosphorylation is important for the therapeutic development of AD. In this study, we investigated the impact of phosphorylated Ser289, one of the 40 aberrant phosphorylation sites of full-length tau proteins, on monomeric and dimeric structures of tau repeat R2 peptides. We carried out intensive replica exchange molecular dynamics simulation with a total simulation time of up to 0.1 ms. Our result showed that the phosphorylation significantly affected the structures of both the monomer and the dimer. For the monomer, the phosphorylation enhanced ordered-disordered structural transition and intramolecular interaction, leading to more compactness of the phosphorylated R2 compared to the wild-type one. As to the dimer, the phosphorylation increased intermolecular interaction and ß-sheet formation, which can accelerate the oligomerization of R2 peptides. This result suggests that the phosphorylation at Ser289 is likely to promote tau aggregation. We also observed a phosphorylated Ser289-Na+-phosphorylated Ser289 bridge in the phosphorylated R2 dimer, suggesting an important role of cation ions in tau aggregation. Our findings suggest that phosphorylation at Ser289 should be taken into account in the inhibitor screening of tau oligomerization.

Coexistence of surface lattice resonances and bound states in the continuum in a plasmonic lattice.

We present a numerical study on a 2D array of plasmonic structures covered by a subwavelength film. We explain the origin of surface lattice resonances (SLRs) using the coupled dipole approximation and show that the diffraction-assisted plasmonic resonances and formation of bound states in the continuum (BICs) can be controlled by altering the optical environment. Our study shows that when the refractive index contrast Δn < -0.1, the SLR cannot be excited, while a significant contrast (Δn > 0.3) not only sustains plasmonic-induced resonances but also forms both symmetry-protected and accidental BICs. The results can aid the streamlined design of plasmonic lattices in studies on light-matter interactions and applications in biosensors and optoelectronic devices.

Multi-locus genome-wide association studies reveal novel alleles for flowering time under vernalisation and extended photoperiod in a barley MAGIC population.

KEY MESSAGE: Key genes controlling flowering and interactions of different photoperiod alleles with various environments were identified in a barley MAGIC population. A new candidate gene for vernalisation requirements was also detected. Optimal flowering time has a major impact on grain yield in crop species, including the globally important temperate cereal crop barley (Hordeum vulgare L.). Understanding the genetics of flowering is a key avenue to enhancing yield potential. Although bi-parental populations were used intensively to map genes controlling flowering, their lack of genetic diversity requires additional work to obtain desired gene combinations in the selected lines, especially when the two parental cultivars did not carry the genes. Multi-parent mapping populations, which use a combination of four or eight parental cultivars, have higher genetic and phenotypic diversity and can provide novel genetic combinations that cannot be achieved using bi-parental populations. This study uses a Multi-parent advanced generation intercross (MAGIC) population from four commercial barley cultivars to identify genes controlling flowering time in different environmental conditions. Genome-wide association studies (GWAS) were performed using 5,112 high-quality markers from Diversity Arrays Technology sequencing (DArT-seq), and Kompetitive allele-specific polymerase chain reaction (KASP) genetic markers were developed. Phenotypic data were collected from fifteen different field trials for three consecutive years. Planting was conducted at various sowing times, and plants were grown with/without additional vernalisation and extended photoperiod treatments. This study detected fourteen stable regions associated with flowering time across multiple environments. GWAS combined with pangenome data highlighted the role of CEN gene in flowering and enabled the prediction of different CEN alleles from parental lines. As the founder lines of the multi-parental population are elite germplasm, the favourable alleles identified in this study are directly relevant to breeding, increasing the efficiency of subsequent breeding strategies and offering better grain yield and adaptation to growing conditions.

Stable Cavitation Interferes with Aß16-22 Oligomerization.

Ultrasound and microbubbles are used for many medical applications nowadays. Scanning ultrasound can remove amyloid-ß (Aß) aggregates in the mouse brain and restores memory in an Alzheimer's disease mouse model. In vitro studies showed that amyloid fibrils are fragmented due to the ultrasound-induced bubble inertial cavitation, and ultrasonic pulses accelerate the depolymerization of Aß fibrils into monomers at 1 µM of concentration. Under applied ultrasound, microbubbles can be in a stable oscillating state or unstable inertial cavitation state. The latter occurs when ultrasound causes a dramatic change of bubble sizes above a certain acoustic pressure. We have developed and implemented a nonequilibrium molecular dynamics simulation algorithm to the AMBER package, to facilitate the investigation of the molecular mechanism of Aß oligomerization under stable cavitation. Our results indicated that stable cavitation not only inhibited oligomeric formation, but also prevented the formation of ß-rich oligomers. The network analysis of state transitions revealed that stable cavitation altered the oligomerization pathways of Aß16-22 peptides. Our simulation tool may be applied to optimize the experimental conditions to achieve the best therapeutical effect.

A multiple-step in silico screening protocol to identify allosteric inhibitors of Spike-hACE2 binding.

While the COVID-19 pandemic continues to worsen, effective medicines that target the life cycle of SARS-CoV-2 are still under development. As more highly infective and dangerous variants of the coronavirus emerge, the protective power of vaccines will decrease or vanish. Thus, the development of drugs, which are free of drug resistance is direly needed. The aim of this study is to identify allosteric binding modulators from a large compound library to inhibit the binding between the Spike protein of the SARS-CoV-2 virus and human angiotensin-converting enzyme 2 (hACE2). The binding of the Spike protein to hACE2 is the first step of the infection of host cells by the coronavirus. We first built a compound library containing 77 448 antiviral compounds. Molecular docking was then conducted to preliminarily screen compounds which can potently bind to the Spike protein at two allosteric binding sites. Next, molecular dynamics simulations were performed to accurately calculate the binding affinity between the spike protein and an identified compound from docking screening and to investigate whether the compound can interfere with the binding between the Spike protein and hACE2. We successfully identified two possible drug binding sites on the Spike protein and discovered a series of antiviral compounds which can weaken the interaction between the Spike protein and hACE2 receptor through conformational changes of the key Spike residues at the Spike-hACE2 binding interface induced by the binding of the ligand at the allosteric binding site. We also applied our screening protocol to another compound library which consists of 3407 compounds for which the inhibitory activities of Spike/hACE2 binding were measured. Encouragingly, in vitro data supports that the identified compounds can inhibit the Spike-ACE2 binding. Thus, we developed a promising computational protocol to discover allosteric inhibitors of the binding of the Spike protein of SARS-CoV-2 to the hACE2 receptor, and several promising allosteric modulators were discovered.

Elastic moduli of normal and cancer cell membranes revealed by molecular dynamics simulations.

Recent studies indicate that there are mechanical differences between normal cells and cancer cells. Because the cell membrane takes part in a variety of vital processes, we test the hypothesis of whether or not two fundamental alterations in the cell membrane, i.e., the overexpression of phosphatidylserine lipids in the outer leaflet and a reduction in cholesterol concentration, could cause the softening in cancer cells. Adopting ten models of normal and cancer cell membranes, we carry out 1 µs all-atom molecular dynamics simulations to compare the structural properties and elasticity properties of two membrane types. We find that the overexpression of the phosphatidylserine lipids in the outer leaflet does not significantly alter the area per lipid, the membrane thickness, the lipid order parameters and the elasticity moduli of the cancer membranes. However, a reduction in the cholesterol concentration leads to clear changes in those quantities, especially decreases in the bending, tilt and twist moduli. This implies that the reduction of cholesterol concentration in the cancer membranes could contribute to the softening of cancer cells.

Molecular dynamics simulation of cancer cell membrane perforated by shockwave induced bubble collapse.

It has been widely accepted that cancer cells are softer than their normal counterparts. This motivates us to propose, as a proof-of-concept, a method for the efficient delivery of therapeutic agents into cancer cells, while normal cells are less affected. The basic idea of this method is to use a water jet generated by the collapse of the bubble under shockwaves to perforate pores in the cell membrane. Given a combination of shockwave and bubble parameters, the cancer membrane is more susceptible to bending, stretching, and perforating than the normal membrane because the bending modulus of the cancer cell membrane is smaller than that of the normal cell membrane. Therefore, the therapeutic agent delivery into cancer cells is easier than in normal cells. Adopting two well-studied models of the normal and cancer membranes, we perform shockwave induced bubble collapse molecular dynamics simulations to investigate the difference in the response of two membranes over a range of shockwave impulse 15-30 mPa s and bubble diameter 4-10 nm. The simulation shows that the presence of bubbles is essential for generating a water jet, which is required for perforation; otherwise, pores are not formed. Given a set of shockwave impulse and bubble parameters, the pore area in the cancer membrane is always larger than that in the normal membrane. However, a too strong shockwave and/or too large bubble results in too fast disruption of membranes, and pore areas are similar between two membrane types. The pore closure time in the cancer membrane is slower than that in the normal membrane. The implications of our results for applications in real cells are discussed in some details. Our simulation may be useful for encouraging future experimental work on novel approaches for cancer treatment.

Machine-learning reinforcement for optimizing multilayered thin films: applications in designing broadband antireflection coatings.

The design and fabrication of nanoscale multilayered thin films play an essential role in regulating the operation efficiency of sensitive optical sensors and filters. In this paper, we introduce a packaged tool that employs flexible electromagnetic calculation software with machine learning in order to find the optimized double-band antireflection coatings in intervals of wavelength from 3 to 5 µm and 8 to 12 µm. Instead of computing or modeling an extremely enormous set of thin film structures, this tool enhanced with machine learning can swiftly predict the optical properties of a given structure with >99.7% accuracy and a substantial reduction in computation costs. Furthermore, the tool includes two learning methods that can infer a global optimal structure or suitable local optimal ones. Specifically, these well-trained models provide the highest accurate double-band average transmission coefficient combined with the lowest number of layers or the thinnest total thickness starting from a reference multilayered structure. Finally, the more sophisticated enhancement method, called the double deep Q-learning network, exhibited the best performance in finding optimal antireflective multilayered structures with the highest double-band average transmission coefficient of about 98.95%.

Molecular Mechanism of Ultrasound-Induced Structural Defects in Liposomes: A Nonequilibrium Molecular Dynamics Simulation Study.

The use of ultrasound in combination with liposomes is a promising approach to improve drug delivery. To achieve an optimal drug release rate, it is important to understand how ultrasound induces pathways on the liposome surface where drugs can be released from the liposome. To this end, we carry out large-scale ultrasound-induced molecular dynamics simulations for three single lipid component liposomes formed from the commonly used phospholipids: 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), 1,2-dipalmitoylphosphatidylcholine (DPPC), or phosphatidylcholine (POPC). The results show that ultrasound induces the detachment of two leaflets of the DOPC surface, suggesting that the drug release pathway may be through the low lipid packing areas on the stretched surface. In contrast, ultrasound induces pore formation on the surface of DPPC and DOPC, where drugs could escape from the liposomes. While the leaflet detachment and transient pore formation are the mechanisms of DOPC and DPPC, respectively, in both liquid-ordered and liquid-disordered phases, the leaflet detachment mechanism is switched to the transient pore formation mechanism on going from the liquid-ordered phase to the liquid-disordered phase in the POPC liposome. By adding 30% mol cholesterol, the leaflet detachment mechanism is observed in all liposomes. We found that the molecular origin that determines a mechanism is the competition between the intraleaflet and interleaflet interacting energy of lipids. The connection to experimental and theoretical modeling is discussed in some detail.

Using chlorate as an analogue to nitrate to identify candidate genes for nitrogen use efficiency in barley.

Nitrogen (N) is one of the most important macronutrients for crop growth and development. Large amounts of N fertilizers are applied exogenously to improve grain yield and quality, which has led to environmental pollution and high cost of production. Therefore, improvement of N use efficiency (NUE) is a very important aspect for sustainable agriculture. Here, a pilot experiment was firstly conducted with a set of barley genotypes with confirmed NUE to validate the fast NUE screening, using chlorate as an analogue to nitrate. High NUE genotypes were susceptible to chlorate-induced toxicity whereas the low NUE genotypes were tolerant. A total of 180 barley RILs derived from four parents (Compass, GrangeR, Lockyer and La Trobe) were further screened for NUE. Leaf chlorosis induced by chlorate toxicity was the key parameter observed which was later related to low-N tolerance of the RILs. There was a distinct variation in chlorate susceptibility of the RILs with leaf chlorosis in the oldest leaf ranging from 10 to 80%. A genome-wide association study (GWAS) identified 9 significant marker-trait associations (MTAs) conferring high chlorate sensitivity on chromosomes 2H (2), 3H (1), 4H (4), 5H (1) and Un (1). Genes flanking with these markers were retrieved as potential targets for genetic improvement of NUE. Genes encoding Ferredoxin 3, leucine-rich receptor-like protein kinase family protein and receptor kinase are responsive to N stress. MTA4H5468 which exhibits concordance with high NUE phenotype can further be explored under different genetic backgrounds and successfully applied in marker-assisted selection. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-021-01239-8.

Spectrum-effect relationship between high-performance thin-layer chromatography data and xanthine oxidase inhibitory activity of celery seed extract.

Celery seeds are medicinal herbs used for the prevention and treatment of gout as these have the ability to inhibit the activity of xanthine oxidase and reduce the concentration of serum uric acid. In this study, the relationship between xanthine oxidase inhibitory effects and high-performance thin-layer chromatography data of celery seed extracts was established using multilayer neural network (MNN) in combination with principal component analysis (PCA). The constructed MNN-PCA model was stable and had accurate prediction ability with coefficient of determination = 0.9998, leave-one-out coefficient = 0.7371, root mean square error = 0.0025, and mean absolute deviation = 0.0019 for the training set and coefficient of determination = 0.8124, root mean square error = 0.0784, and mean absolute deviation = 0.0645 for the test set. This model can be used to identify the main compounds related to the xanthine oxidase inhibitory effect of celery seed extract. These results can be applied not only to celery extract but also to other herbal medicines.

Correction to: Identifying miRNA synergism using multiple-intervention causal inference.

After publication of this supplement article [1], it was brought to our attention that the Fig. 3 was incorrect. The correct Fig. 3 is as below.

Molecular mechanism of ultrasound interaction with a blood brain barrier model.

The brain is strictly protected by the blood brain barrier preventing the crossing of therapeutics to treat brain diseases. The high and low intensity focused ultrasound methods have been used to temporarily open the blood brain barrier, facilitating the transport of drugs. The methods are very promising because the opening is transient, localized, and noninvasive. However, the molecular mechanism of the opening is unknown, and this limits the development and application of these methods. With this in mind, we carry out a molecular dynamics simulation study to understand the interaction of ultrasound with the cell membrane and the tight junction. Our minimal blood brain barrier model is composed of two lipid bilayers, mimicking two portions of neighboring cells, connected together by a tight junction formed by a pair of two cis-dimers of the claudin-5 protein. Using an experimental ultrasound frequency of 50 MHz, simulations show that at low intensities, ultrasound does not impact the structure of the cell membranes and tight junction, implying that the direct interaction of ultrasound with the blood brain barrier is not responsible for the experimentally observed opening. At high intensities, the ultrasound pulls the monolayers of individual cell membrane lipid bilayers apart, creating air compartments inside the bilayers. This reduces the free energy barrier for the translocation of drugs across the lipid bilayer and enhances drug permeability. At very high intensities, the two monolayers are largely separated, resulting in cell damage and implying that the blood brain barrier is primarily opened at the experimentally observed damaged areas.

E-motif formed by extrahelical cytosine bases in DNA homoduplexes of trinucleotide and hexanucleotide repeats.

Atypical DNA secondary structures play an important role in expandable trinucleotide repeat (TR) and hexanucleotide repeat (HR) diseases. The cytosine mismatches in C-rich homoduplexes and hairpin stems are weakly bonded; experiments show that for certain sequences these may flip out of the helix core, forming an unusual structure termed an 'e-motif'. We have performed molecular dynamics simulations of C-rich TR and HR DNA homoduplexes in order to characterize the conformations, stability and dynamics of formation of the e-motif, where the mismatched cytosines symmetrically flip out in the minor groove, pointing their base moieties towards the 5'-direction in each strand. TRs have two non-equivalent reading frames, (GCC)n and (CCG)n; while HRs have three: (CCCGGC)n, (CGGCCC)n, (CCCCGG)n. We define three types of pseudo basepair steps related to the mismatches and show that the e-motif is only stable in (GCC)n and (CCCGGC)n homoduplexes due to the favorable stacking of pseudo GpC steps (whose nature depends on whether TRs or HRs are involved) and the formation of hydrogen bonds between the mismatched cytosine at position i and the cytosine (TRs) or guanine (HRs) at position i - 2 along the same strand. We also characterize the extended e-motif, where all mismatched cytosines are extruded, their extra-helical stacking additionally stabilizing the homoduplexes.