Frontiers in Molecular Biosciences

Tyrosine Kinase inhibitors (TKIs) are widely used as effective chemotherapeutic agents for treating patients with EGFR-mutated NSCLC. Unfortunately, after treatment, patients eventually develop resistance to TKI therapy. The most common resistance mechanism for the TKI Osimertinib is the overexpression of the MET Proto-Oncogene, Receptor Tyrosine Kinase (MET). We previously demonstrated that miR-411-5p A-to-I edited at position 5 (miR-411ed) can directly target MET in A549 and H1299 cells. MiR-411ed in combination with Osimertinib reduced cell proliferation in two TKI resistant EGFR-mutated cell lines: HCC827R and PC9R. MiR-411ed did not downregulate MET expression in HCC827R, suggesting an alternative mechanism for TKI response. In this study, we aim to identify the mechanism of miR-411ed TKI response using a multi-omics approach of RNAseq and protein mass spectrometry. In our cellular model, we identified miR-411ed affected genes independent of MET activity, resulting in 211 genes (RNAseq) and 36 proteins (proteomics). Pathway analysis identified an increase in interferon signaling for RNAseq and combined omics, and a decrease in ERK/MAPK signaling in proteomics. Using the IsoTar target prediction tool, we identified STAT3 as a key regulator and confirmed STAT3 protein downregulation upon transfection with miR-411ed. We further investigated the effect of miR-411ed in vivo, observing a reduction in tumor size with miR-411ed in combination with Osimertinib but not with miR-411ed or Osimertinib treatment alone, confirming the effectiveness of miR-411ed in TKI response.

Staphylococcus aureus (S. aureus) is an opportunistic pathogen that is a global health concern for its ability to cause a wide spectrum of clinical infections. Due to the emergence of resistance to commonly used antibiotics, there has been interest in exploring the use of antimicrobial peptides to treat S. aureus infections. However, changes in the lipid composition of the lipid bilayer membrane can alter the activity of peptides, and S. aureus is able to induce variations in lipid composition in response to environmental stress. Here, we explore how the main lipid components in S. aureus are altered when exposed to LL-37, a human cathelicidin involved in primary immune response, and ATRA-1, a short antimicrobial peptide derived from the snake Naja atra venom. A lipidomic study is conducted through HPLC-MS-MS (LC-ESI-MS/MS) to quantify phosphatidylglycerol, cardiolipin, lysyl-phosphatidylglycerol, monogalacto- and digalacto-diacylglycerol, and carotenoids. In addition, menaquinones, responsible for electron transport during oxidative phosphorylation, were also quantified. Biophysical properties such as membrane electric surface potential and lipid packing were assessed. We find that lipid adaptation is specific to the type of antimicrobial peptide, where ATRA-1 mainly induces changes in the electric surface potential through variations in Lysyl-PG, while exposure to LL-37 changes carotenoid levels, inducing an increase in membrane rigidity as measured by FTIR. In addition, both peptides induce a reduction in menaquinone and DGDG levels. These findings highlight the role of membrane lipid remodeling as a peptide-specific response mechanism in S. aureus, with implications for the development of AMP-based therapies. HighlightsO_LIStaphylococcus aureus responds through shifts in lipid composition and membrane biophysical properties to exposure to the antimicrobial peptides LL-37 and ATRA-1. C_LIO_LIBoth LL-37 and ATRA-1 lead to shifts in the glycolipids MGDG and DGDG; two lipids involved in regulating negative membrane curvature stress and responsible for shifting resistance to antimicrobial peptide activity in Staphylococcus aureus. C_LIO_LILL-37 treatment leads to an overall reduction in carotenoid content in Staphylococcus aureus, including the carotenoid end-product staphyloxanthin and the precursor 4,4-diaponeurosporenoic acid. Both lipids regulate membrane biophysical properties and protect Staphylococcus aureus from oxidative stress. C_LIO_LIBoth LL-37 and ATRA-1 lead to a reduction in menaquinone levels, which are involved in the electron transport chain during oxidative phosphorylation. Reduction in these menaquinones have been associated to the formation of small colony variants that are often observed in chronic Staphylococcus aureus infections. C_LI

Staphylococcus aureus (S. aureus) is a clinically relevant pathogen capable of adapting its membrane composition in response to environmental stress. In this adaptive process, bacterial carotenoids play a crucial role. Although staphyloxanthin (STX) is the main carotenoid produced by the bacterium, S. aureus also synthesizes other pigmented intermediates that play an unknown role in regulating membrane biophysical properties. In this study, we purified 4,4-diaponeurosporenoic acid (4,4'-DNPA) from S. aureus carotenoid extracts and evaluated its effect on the thermotropic and biophysical properties of representative membrane models. The highly rigid triterpenoid 4,4'-DNPA is one of the last precursors in the biosynthesis of STX and is found in high concentrations in the stationary phase of S. aureus. Phase transition temperatures were determined using infrared spectroscopy, while interfacial hydration and hydrophobic core dynamics were investigated using fluorescence spectroscopy through Laurdan generalized polarization and DPH anisotropy. The results show that 4,4'-DNPA increases the main phase transition temperature of lipid bilayers in a concentration-dependent manner. This is in contrast to STX that decreases the transition temperature. This difference is consistent with the additional fatty acid present in STX that changes its effect on the phase behavior. Furthermore, 4,4'-DNPA reduced the interfacial hydration levels and restricted hydrophobic-core dynamics at higher concentrations, consistent with increased molecular order and stability. 4,4'-DNPA therefore complements STX in increasing membrane order and lipid packing. These findings support the notion that the production of bacterial carotenoids functions as a biophysical regulatory mechanism of lipid packing in S. aureus membranes.

The HIV-1 Vpu protein aids viral adaptation by influencing host cell pathways via protein interactions. While Vpu is mainly found in plasma and endomembranes, we recently discovered a soluble form that forms a stable, equimolar complex with Ca2+-bound calmodulin (Ca2+-CaM), potentially affecting Vpus cellular trafficking. Here, to determine the binding affinity and identify regions of soluble Vpu involved in CaM binding, we used ensemble Forster Resonance Energy Transfer (eFRET). We tested Cy3-labeled full-length (FL) Vpu, a C-terminal fragment (helices 2 and 3), and a Cy3-labeled FL Vpu V22A/W23Y mutant with substitutions in Vpus helix 1. All Vpus variants were labeled at residue L42C, and Ca2+-CaM was tagged with Cy5 at residue S39C. eFRET analysis of 100 nM Cy3-Vpu variants mixed with Cy5-Ca2+-CaM (in the range 100-600 nM) revealed dissociation constants (Kd) and binding energies ({Delta}G) for heterocomplexes. FL Vpu-Ca2+-CaM showed high stability (Kd [~]40 nM,{Delta} G [~]10.1 kcal/mol), while the truncated C-terminal region and V22A/W23Y mutant formed less stable complexes with Ca2+-CaM (Kd[~]200 nM and 800 nM,{Delta} G [~]9 kcal/mol and [~]8.3 kcal/mol). This, a binding hot spot in Vpus CaM-binding motif in helix 1 was identified, which may control the stability of Vpu-Ca2+-CaM complex and Vpus insertion in the membrane: We hypothesize that upon delivery to the membrane, the hydrophobic helix 1 of Vpu dissociates from Ca2+-CaM and inserts in the lipid bilayer; thereafter, CaM dissociates from Vpu facilitated by the reduced Vpu-Ca2+-CaM complex stability. The findings from this study advance our understanding of HIV-1 Vpu interactions with cellular components and may aid the development of antivirals.

Inflammasomes are cytosolic innate immune sensors that, once activated by a pathogenic threat, lead to activation of the inflammatory Caspase-1. Inflammasome activation and its consequences have been studied extensively in myeloid cells and in overexpression systems. Recent studies have identified cell type specific effects that are not fully explained by the known cleavage targets of Caspase-1. Here, we identified targets of caspase cleavage using mass spectrometry in primary intestinal epithelial cells by specifically activating the NAIP-NLRC4 inflammasome. We have taken an unbiased approach and developed a novel method for analyzing mass spectrometry data for evidence of caspase activity. Our approach can also be applied to existing proteomic datasets to establish the presence of caspase activity under various biological conditions. These results lay the groundwork for future studies on mechanisms of caspase-induced processes such as intestinal epithelial cell extrusion.

Although several ancillary tests are available in limited laboratories, diagnosis of microphthalmia (MiT)/TFE family translocation renal cell carcinoma (tRCC) could be challenging due to diverse and overlapping tumor morphology and the lack of reliable biomarkers. GPNMB has been recently identified as a diagnostic marker for various renal neoplasms with FLCN/TSC/mTOR-TFE alterations. However, the sensitivity and specificity of GPNMB immunostain are suboptimal and the result interpretation in ambiguous cases could be difficult. To search additional biomarkers that could improve the screening sensitivity and predict genetic aberrations in FLCN/TSC/mTOR-TFE pathway in renal tumors, we performed bioinformatic analysis of publicly available cancer databases and found GPR143, a transmembrane protein regulated by MiT transcription factors, was highly expressed in a subset of renal cell carcinomas (RCCs). In two the Cancer Genome Atlas (TCGA) kidney cancer cohorts, RCCs with high levels of GPR143 expression were enriched for renal neoplasms with FLCN/TSC/mTOR-TFE alterations. Similar to GPNMB labeling, GPR143 immunostain was positive in the majority of tRCC cases and renal tumors with FLCN/TSC/mTOR alterations, suggesting that GPR143 could function as another surrogate marker for FLCN/TSC/mTOR-TFE alterations in certain renal tumors. Interestingly, despite the concordant GPR143 and GPNMB immunoreactivity in most renal neoplasms with FLCN/TSC/mTOR-TFE alterations, diffuse GPR143 immunostain was observed in some cases with negative or focal GPNMB labeling. Taken together, our results indicate GPR143 could serve as a useful adjunct marker to improve the sensitivity for screening renal tumors with FLCN/TSC/mTOR-TFE alterations.

Intercalation of small molecules between DNA base pairs affects DNA conformation, disrupting essential cellular processes including replication, transcription, and repair. We investigated conformational changes in 18-mer DNA upon intercalation of doxorubicin, SYBR Gold and YOYO-1 using extensive MD simulations. Two main patterns for the intercalation were identified: RISE-type intercalation occurs between adjacent base pairs and extends the DNA helix with decreased twist angles, while OPEN-type intercalation proceeds through base-pair opening without significant DNA extension. Kinetic analysis revealed that association rates for intercalation followed the order: first YO-moiety (mono-intercalation) > SYBR Gold > doxorubicin > YOYO-1 (bis-intercalation). Free energy landscape showed that forces at DNA termini reached up to 117 pN during stretching. Notably, base pairs adjacent to intercalators were protected from strand separation, accompanied by additional helical unwinding. MM-PBSA/GBSA analysis revealed that the driving force for intercalation is the stacking energy, and the binding affinity was highest for minor groove binding. Persistence length decreased with single molecule binding but recovered with two molecules due to their electrostatic repulsion. Mechanical properties of intercalated DNA showed position-dependence, demonstrating that multiple intercalation modes coexist in solution. The heterogeneous nature of intercalation explains why experimental measurements reflect ensemble averages rather than single binding configurations.

This study investigates the interaction between the cationic antimicrobial peptide protamine and bacterial porin OmpF (E. coli) at the single-molecule level. Using high-resolution conductance measurements in planar lipid bilayers, strong voltage- and concentration-dependent ion current blockages with OmpF, indicating significant protamine binding were observed. Further analysis revealed that peptide length influences binding kinetics, with longer peptides showing reduced affinity and slower exchange rates. These findings demonstrate that OmpF is a tractable model for studying cationic peptide-channel interactions and translocation mechanisms relevant to antimicrobial action.

1Long QT syndrome Type 2 (LQT2) is a genetic disorder caused by missense mutations in the KCNH2 gene that encodes the potassium channel KV11.1. Previous studies have shown that most KV11.1 missense mutations with loss-of-function phenotypes result from impaired trafficking from the endoplasmic reticulum to the plasma membrane. To investigate the molecular basis of these defects, we used molecular dynamics simulations to analyze two sets of disease-associated missense mutations: those that suppress and those that maintain normal channel trafficking. We focused initially on the conformational and dynamics differences between wild-type and several mutants of KV11.1 via molecular dynamics simulations when two K+ were placed in the selectivity filter (SF). Our study reveals that missense mutations in the S4 helix allosterically disrupt the selectivity filter, a critical determinant for proper channel trafficking. Trafficking-competent variants largely retained a wild-type selectivity filter structure, whereas trafficking-deficient mutants exhibited pronounced structural perturbations in this region. These findings suggest that certain LQT2-associated missense mutations in KCNH2 impair channel trafficking by compromising the structural integrity of the selectivity filter. We additionally found that second-site variants Y652C in the drug binding vestibule can correct structural defects associated with some mistrafficking variants.

Monte Carlo neutron ray-tracing simulations of time-of-flight (TOF)-Laue neutron macromolecular crystal diffraction (n-MX) using the McStas software package were done for the upcoming NMX Macromolecular Diffractometer at the European Spallation Source. Splitting neutron rays that arrive at the crystal lead to dramatic improvements in event formation with minimal computational overhead. The simulated event probability data was sampled using a new single-pass weighted reservoir sampling method, and processed like real n-MX data using DIALS. The effects of air and beamstop scatter on simulated data was investigated. SynopsisMonte Carlo simulations of neutron protein diffraction experiments provide useful data that models instrumental components that interact with neutrons, as well as the crystal diffraction itself. These data can be applied to instrument development, such as the commissioning of the NMX Macromolecular Diffractometer at ESS.

Ubiquitin-like protein 3 (UBL3) is a post-translational modifier that sorts proteins into small extracellular vesicles and regulates the trafficking of disease-associated proteins such as -synuclein. The structural and dynamic features of the UBL domain that underlie these functions, however, remain poorly understood. Here we performed in silico structural dynamics analysis of the UBL3 UBL domain using an NMR structure ensemble combined with anisotropic network modeling (ANM) and perturbation response scanning (PRS). Principal component analysis and residue-wise fluctuation analysis consistently revealed high flexibility in the C-terminal region of UBL3. Comparative ANM analysis across 20 ubiquitin-like proteins (UBLs) further showed that C-terminal flexibility is a conserved yet variable property within the UBL family. PRS analysis demonstrated that residues forming the central -helix of the {beta}-grasp fold exert greater dynamic control over collective motions than {beta}-sheet residues. Notably, UBL3 displayed the highest helix/sheet PRS effectiveness ratio among all UBLs analyzed, highlighting the prominent dynamic contribution of helix residues in this domain. Together, these results provide a structural basis for understanding UBL3-dependent protein interactions and disease-related mechanisms, and suggest that helix-centered dynamic control in the UBL domain may represent a potential target for modulating UBL3 function.

Protein recognition mimicry is of great interest in the field of molecular bioengineering and rational design, with mutagenesis frequently employed to analyze the effects of altering amino acids involved in molecular recognition. The conformational and energetic effects of such alterations can be investigated in detail with the help of molecular dynamics (MD) methodologies. While existing MD-based computational tools can be used to explore a particular set of mutations at a time, suitable for small-scale studies, high-throughput (HTP) exploration of protein recognition for engineering purposes would greatly benefit from an integrative platform that streamlines preparation, mutagenesis, simulation and post-processing of up to several thousand molecular systems, along with robust tools for comprehensive and straightforward comparative analysis. DyME (Dynamic Mutagenesis Engine) is a distributed platform that enables systematic investigations of protein recognition mimicry by combining HTP mutagenesis, solvated MD simulations and a Toolbox for comparative analysis (TCA), including interfacial water-site mapping. DyME uses 3D structural information of any protein-protein or protein-DNA complex as input. Its automated MD-based mutagenesis engine facilitates systematic investigation of how site-specific alterations affect recognition, enabling the organization of single, double and triple modifications into combinatorial libraries for comprehensive comparative analysis. In DyME, relevant MD trajectory-derived data is scavenged and stored into a central database, providing aggregation capabilities that ease multi-feature analysis across an extensive collection of simulations. An interactive web-GUI and specialized widgets simplify preparation and efficient molecular and numerical comparative exploration. DyMEs capabilities are evaluated using available experimental data. Its source code is available at https://github.com/pisabarro-group/DYME

Mutation-induced drug resistance is a major contributor to the failure of targeted cancer therapies, particularly in tumors driven by mutations in the KRAS oncogene. Although covalent inhibitors effectively target KRAS G12C, secondary mutations such as G12C/Y96C, G12C/Y96S, and G12C/Y96D lead to resistance despite leaving the covalent attachment site intact. To predict these resistance outcomes, we developed a computational framework that integrates molecular dynamics-derived structural, energetic, thermodynamic, and contact-based descriptors with machine learning. Features extracted from simulations of treatment-sensitive and treatment-resistant KRAS mutants were used to train logistic regression, random forest, support vector machine, and Bayesian Network classifiers, achieving average accuracies above 90%. Solvent-accessible surface area variability, Lennard-Jones 1,4 energy, mean square displacement, and root mean square fluctuation emerged as the most discriminatory features. Residues G10, E62, and H95 showed the highest predictive value. This approach highlights conformational and solvent-exposure changes as central drivers of KRAS drug resistance and provides a generalizable workflow for other clinically relevant mutant targets. Author SummaryMutation-induced resistance is a common challenge across many cancer types and is often associated with aggressive tumor progression and poor therapeutic response. Investigating the dynamic properties of proteins harboring such mutations provides valuable insights into the structural and functional consequences of these alterations, thereby helping to elucidate the mechanisms of drug resistance. Machine learning algorithms are particularly effective at uncovering complex patterns within high-dimensional data, such as molecular dynamics simulation trajectories. Integrating these algorithms with analysis of protein dynamics holds significant potential to aid in drug discovery challenges by reducing both time and resource demands while increasing the likelihood of identifying effective therapeutic candidates. As a proof of concept, we developed a computational framework that integrates molecular dynamics-derived molecular features with machine learning to distinguish treatment-sensitive from treatment-resistant KRAS mutants. KRAS is known for drug resistance arising from secondary mutations that disrupt inhibitor binding despite intact covalent attachment sites. The models achieved over 90% accuracy and identified solvent-exposure and conformational changes at residues G10, E62, and H95 as key predictors of treatment resistance. This workflow offers a generalizable strategy for understanding and forecasting mutation-induced resistance.

The NAD+ dependent deacetylase sirtuin-1 (SIRT1) is known to elicit cellular defenses against aging, cancer, and other aberrant pathologies. Previous studies have identified an intrinsically disordered region of SIRT1 comprised of N-terminal residues 1-52, herein referred to as motif A, which activates SIRT1 activity, likely through intramolecular interactions. Additionally, phosphorylation of N-terminal residues Ser27 and Ser47 has been shown to be important for regulating SIRT1 activity and stability. The lack of in vitro characterization of these effects hampers our further understanding of the role of motif A in SIRT1 regulation. In this study, we elucidate the role phosphorylation plays in motif As structure as well as its regulatory effects on SIRT1 activity against Ac-p65. We find that phosphomimetic mutation at Ser27 significantly increases the activation effect of motif A towards SIRT1. This result is supported by molecular dynamics simulations of the phosphomimetics, which reveal stabilization of different transient structures for motif A depending on whether Ser27 and Ser47 have been modified. A key finding suggested by this study is that phosphorylation of S27 appears to activate SIRT1 by causing motif A, which is intrinsically disordered in the WT, to fold into an ordered structure. This conclusion is based on both the experimental findings and simulation results. These findings contribute to our understanding of SIRT1 regulation, specifically the role played by phosphorylation within the N-terminal disordered region.

Intrinsically disordered proteins (IDPs) play an important role in a wide range of biological functions and are linked to several diseases. Due to technical difficulties and the high cost of experimental determination of disorder in proteins, combined with the exponential increase of unannotated protein sequences, the development of computational methods for disorder prediction became an active area of research in the last few decades. In this work, we present emb2dis, a deep learning model that uses protein language models (pLMs) to predict disorder from sequence. The emb2dis tool is a pre-trained model that receives as input a protein sequence, calculates its pLM embedding and passes it to a deep learning model. In contrast to existing approaches, emb2dis integrates informative sequence representations with a novel architecture that combines residual networks (ResNets) and dilated convolutions. This design effectively enlarges the receptive field of the convolution operation, enabling the model to better capture an extended context of each amino acid. At the output, emb2dis assigns a disorder propensity score to each residue in the sequence. The model was evaluated on datasets from the latest CAID3 blind benchmark for disorder prediction, where it achieved first place in the Disorder-PDB category, exhibiting strong performance with high AUC and Fmax scores. Additionally, it ranked among the top ten methods on the Disorder-NOX dataset. We provide a freely available web-demo for emb2dis and a source code repository for local installation. Weblink for the toolhttps://sinc.unl.edu.ar/web-demo/emb2dis/ The importance of the emb2dis tool is that it provides a new deep learning approach and significant improvements in the prediction of protein disorder, with a simple web interface and graphical output detailing per-residue disorder.

A morphospace is an abstract space of theoretically possible biological traits, shapes, or property values. It is interesting to explore which parts of a morphospace life occupies, as compared to those parts which could be occupied, but are not. Comparing random and natural non-coding (nc) RNA secondary structures is an established approach to studying morphospace occupation for RNA structures. Most earlier studies have focused on the minimum free energy (MFE) structure, while relatively few have looked at the Boltzmann distribution, describing the ensemble of energetically suboptimal RNA folds. These suboptimal structures may have important roles and functions, and hence should be examined carefully. Here we compare random and natural ncRNA in terms of their Boltzmann distributions, finding that natural RNA tend to have very similar profiles to random RNA, with the main difference being that natural RNA are slightly more energetically stable, except for very short sequences (20 to 30 nucleotides) which tend to be slightly less stable. We infer that natural ncRNA occupy similar parts of the morphospace that random RNA do, indicating that the biophysics of the genotype-phenotype map largely determines the ensemble properties of ncRNA.

Protein function often involves multiple conformational states. Several multiple sequence alignment-perturbing strategies, including stochastic subsampling, clustering, and column masking, have been shown to enhance AlphaFold2 (AF2) sampling of alternative protein states. Here, we evaluate these strategies on AlphaFold3 (AF3) and compare their performance with the BioEmu Boltzmann sampling model on 107 proteins with multiple experimentally solved conformational states. We find that unperturbed AF3 samples alternative states with significantly higher TM-scores compared to AF2 and comparable to BioEmu. In particular, all MSA perturbation methods improve AF3 sampling at a statistically significant level, improving the top 1% TM-score by at least 0.05 in approximately 20% of cases each, while rarely worsening the performance. Furthermore, we find that different choices of amino acid masks can improve column-masked AF3 sampling for specific targets. Our results highlight how MSA perturbations remain relevant in AF3, providing a useful tool for understanding dynamic biological processes.

Ion channels are promising targets for drug discovery due to their diverse physiological functions. The database of ion channel structures has grown exponentially over the last decade due to advances in structure-determination techniques. However, not all ion channel conformations have been determined, and not all druggable conformations can be modelled as thermodynamically stable under simulation conditions. This greatly limits conformation-specific drug targeting. In this study, we used an endogenous regulator of ion channels, phosphatidylinositol-4,5-bisphosphate (PIP2), as a computational tool to probe the open-state conformation of the TMEM16A calcium-activated chloride channel. By using the PIP2-binding conformation from a coarse-grained model, followed by fluctuation-amplified specific traits (FAST) adaptive sampling in an all-atom configuration, the system transitioned from a closed state to a thermodynamically stable open state. The transition also highlights the importance of PIP2 in TM6 helical kink, the opening of the outer gate and the alpha helix on I551. The open-state structure displays the experimental conductance. Using an accelerated weighted histogram (AWH), the binding site of 1PBC, A9C, niclosamide and Ani9 pore blockers were determined and validated against previous experimental studies. This paves the way to structure-specific drug development, as overactivation of TMEM16A is correlated with many diseases, such as pulmonary hypotension and ischemic stroke. Together, this study highlights the importance of lipids in stabilising ion channel conformations for targeted drug design and introduces a novel approach to expand the therapeutic targeting of ion channels. Significance StatementIon channels are plasma membrane proteins that regulate multiple critical cellular processes. A major obstacle in ion channel-based therapeutic development is the limited pool of thermodynamically stable conformations in the protein structure database, which hampers the accurate use of molecular dynamics simulations to guide structure-based drug design effectively. Using PIP2-assisted adaptive sampling, this study captures the thermodynamically stable open and intermediate state of the TMEM16A channel during channel opening. Developing this novel approach, using a common endogenous ligand, PIP2, highlights the key role of lipid in stabilizing ion channel conformation and thus how conformational specificity provides a critical aspect in ion channel therapeutic development.

Formation of the {beta}-amyloid (A{beta}) plaques is a pathological hallmark of Alzheimers disease (AD), and is believed to be a primary cause of dementia in elderly individuals. In the present work, we simulated the conformational evolution of A{beta}42 dimers in solution and in membrane-like environment to explore the folding of A{beta}42 along fibrillation. The molecular dynamics (MD) simulation was steered by experimental internuclear distance restraints obtained using solid-state nuclear magnetic resonance (ssNMR) spectroscopy. Our results revealed that several hydrophobic and polar motifs within the A{beta}42 sequence played key roles in the early-stage nucleation process of fibrillation and those motifs are also the stabilizing agents in the mature fibrils judged by the energy contribution. Our results also indicated that the peptide association with membrane bilayers could modulate the structural evolution pathways towards fibrillation. These findings contributed to a better understanding of the molecular level structural polymorphisms inherent to A{beta}42 fibrils. Further, the current work demonstrated that the combination of MD simulations with ssNMR-based experimental restraints provided a reliable method for studying structural changes of A{beta}. HighlightO_LIUsing solid-state NMR restraints guided molecular dynamic simulation, {beta}-amyloid dimers displayed consistent {beta}-strand-prone regions, which are major stabilizing segments for mature fibrils. C_LIO_LI{beta}-amyloid dimers evolved differently with or without interacting with the lipid bilayers. C_LIO_LIExperimental restraints guided simulation provided molecular level insights about early-stage interactions along the progress of {beta}-amyloid fibrillation C_LI

Osteosarcoma (OS) is an aggressive bone cancer that most commonly affects children and young adults. OS exhibits a high degree of genomic complexity, as well as cellular plasticity, and dynamic transcriptional regulation is suggested to contribute to treatment resistance and metastasis. Cell lines are well characterized as models to advance our knowledge on OS biology. HOS and U2OS cells have increased invasiveness and higher migratory ability compared with MG63. In this study, we employed a tandem array of consensus transcription factor response elements (catTFREs) proteomic approach to characterize transcription factor (TF) regulatory networks related to OS aggressiveness. We mapped 7,594 proteins and enriched 352 transcription factors and coregulators. When we integrated proteomics with cell line specific gene expression and chromatin accessibility we classified the proteins into different OS cell line dependent sub-clusters and identified TFs and coregulators common for all cell lines and specific for individual cell lines. We demonstrate that RUNX2, MYBL2 and HMGA2 are specifically enriched in HOS and U2OS and may be linked to the cell aggressiveness. ETV5, JUNB, NFIX and ZEB1 were among TFs specific to MG63. Our analysis provides a more comprehensive understanding of the transcriptional drivers that shape OS regulatory landscapes and may have future therapeutic implications.