Macromolecular Structures Research Articles

Tpr Misregulation in Hippocampal Neural Stem Cells in Mouse Models of Alzheimer's Disease.

Nuclear pore complexes (NPCs) are highly dynamic macromolecular protein structures that facilitate molecular exchange across the nuclear envelope. Aberrant NPC functioning has been implicated in neurodegeneration. The translocated promoter region (Tpr) is a critical scaffolding nucleoporin (Nup) of the nuclear basket, facing the interior of the NPC. However, the role of Tpr in adult neural stem/precursor cells (NSPCs) in Alzheimer's disease (AD) is unknown. Using super-resolution (SR) and electron microscopy, we defined the different subcellular localizations of Tpr and phospho-Tpr (P-Tpr) in NSPCs in vitro and in vivo. Elevated Tpr expression and reduced P-Tpr nuclear localization accompany NSPC differentiation along the neurogenic lineage. In 5xFAD mice, an animal model of AD, increased Tpr expression in DCX+ hippocampal neuroblasts precedes increased neurogenesis at an early stage, before the onset of amyloid-β plaque formation. Whereas nuclear basket Tpr interacts with chromatin modifiers and NSPC-related transcription factors, P-Tpr interacts and co-localizes with cyclin-dependent kinase 1 (Cdk1) at the nuclear chromatin of NSPCs. In hippocampal NSPCs in a mouse model of AD, aberrant Tpr expression was correlated with altered NPC morphology and counts, and Tpr was aberrantly expressed in postmortem human brain samples from patients with AD. Thus, we propose that altered levels and subcellular localization of Tpr in CNS disease affect Tpr functionality, which in turn regulates the architecture and number of NSPC NPCs, possibly leading to aberrant neurogenesis.

Macromolecular Engineered Multifunctional Room-Temperature Phosphorescent Polymers through Reversible Deactivation Radical Polymerization.

Despite the intensive research in room-temperature phosphorescent (RTP) polymers, the synthesis of RTP polymers with well-defined macromolecular structures and multiple functions remains a challenge. Herein, reversible deactivation radical polymerization was demonstrated to offer a gradient copolymer (GCP) architecture with controlled heterogeneities, which combines hard segment and flexible segment. The GCPs would self-assemble into a multiphase nanostructure, featuring tunable stretchability, excellent RTP performance, and intrinsic healability without compromising light emission under stretching. The mechanical performance is tunable on demand with elongation at break ranging from 5.0% to 221.7% and Young's modulus ranging from 0.5 to 225.0 MPa.

Investigations into the characterization, degradation, and applications of biodegradable polymers by mass spectrometry.

Biodegradable polymers have been getting more and more attention because of their contribution to the plastic pollution environmental issues and to move towards a circular economy. Nevertheless, biodegradable materials still exhibit various disadvantages restraining a widespread use in the market. Therefore, additional research efforts are required to improve their performance. Mass spectrometry (MS) affords a relevant contribution to optimize biodegradable polymer synthesis, to confirm macromolecular structures, to examine along the time the progress of degradation processes and highlight advantages and drawbacks in the extensive applications. This review aims to provide an overview of the MS investigations carried out to support the synthesis of biodegradable polymers, with helpful information on undesirable products or polymerization mechanism, to understand deterioration pathways by the structure of degradation products and to follow drug release and pharmacokinetic. Additionally, it summarizes MS studies addressed on environmental and health issues related to the extensive use of plastic materials, that is, potential migration of additives or microplastics identification and quantification. The paper is focused on the most significant studies relating to synthetic and microbial biodegradable polymers published in the last 15 years, not including agro-polymers such as proteins and polysaccharides.

Advancing Antiamyloidogenic Activity by Fine-Tuning Macromolecular Topology.

Amyloid β peptide can aggregate into thin β-sheet fibrils or plaques deposited on the extracellular matrix, which is the hallmark of Alzheimer's disease. Multifunctional macromolecular structures play an important role in inhibiting the aggregate formation of amyloidogenic materials and thus are promising candidates with antiamyloidogenic characteristics for the development of next-generation therapeutics. In this study, we evaluate how small differences in the dendritic topology of these structures influence their antiamyloidogenic activity by the comparison of "perfectly dendritic" and "pseudodendritic" macromolecules, both decorated with mannose units. Their compactness, the position of surface units, and the size of glyco-architectures influence their antiamyloidogenic activity against Aβ 40, a major component of amyloid plaques. For the advanced analysis of the aggregation of the Aβ peptide, we introduce asymmetric flow field flow fractionation as a suitable method for the quantification of large and delicate structures. This alternative method focuses on the quantification of complex aggregates of Aβ 40 and glycodendrimer/glyco-pseudodendrimer over different time intervals of incubation, showing a good correlation to ThT assay and CD spectroscopy results. Kinetic studies of the second-generation glyco-pseudodendrimer revealed maximum inhibition of Aβ 40 aggregates, verified with atomic force microscopy. The second-generation glyco-pseudodendrimer shows the best antiamyloidogenic properties confirming that macromolecular conformation in combination with optimal functional group distribution is the key to its performance. These molecular properties were validated and confirmed by molecular dynamics simulation.

Critical assessment of methods of protein structure prediction (CASP)-Round XV.

Computing protein structure from amino acid sequence information has been a long-standing grand challenge. Critical assessment of structure prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. Experiments are conducted every 2 years. The 2020 experiment (CASP14) saw major progress, with the second generation of deep learning methods delivering accuracy comparable with experiment for many single proteins. There is an expectation that these methods will have much wider application in computational structural biology. Here we summarize results from the most recent experiment, CASP15, in 2022, with an emphasis on new deep learning-driven progress. Other papers in this special issue of proteins provide more detailed analysis. For single protein structures, the AlphaFold2 deep learning method is still superior to other approaches, but there are two points of note. First, although AlphaFold2 was the core of all the most successful methods, there was a wide variety of implementation and combination with other methods. Second, using the standard AlphaFold2 protocol and default parameters only produces the highest quality result for about two thirds of the targets, and more extensive sampling is required for the others. The major advance in this CASP is the enormous increase in the accuracy of computed protein complexes, achieved by the use of deep learning methods, although overall these do not fully match the performance for single proteins. Here too, AlphaFold2 based method perform best, and again more extensive sampling than the defaults is often required. Also of note are the encouraging early results on the use of deep learning to compute ensembles of macromolecular structures. Critically for the usability of computed structures, for both single proteins and protein complexes, deep learning derived estimates of both local and global accuracy are of high quality, however the estimates in interface regions are slightly less reliable. CASP15 also included computation of RNA structures for the first time. Here, the classical approaches produced better agreement with experiment than the new deep learning ones, and accuracy is limited. Also, for the first time, CASP included the computation of protein-ligand complexes, an area of special interest for drug design. Here too, classical methods were still superior to deep learning ones. Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.

Gag Virus-like Particles Functionalized with SARS-CoV-2 Variants: Generation, Characterization and Recognition by COVID-19 Convalescent Patients' Sera.

The robustness, safety, versatility, and high immunogenicity of virus-like particles (VLPs) make them a promising approach for the generation of vaccines against a broad range of pathogens. VLPs are recombinant macromolecular structures that closely mimic the native conformation of viruses without carrying viral genetic material. Particularly, HIV-1 Gag-based VLPs are a suitable platform for the presentation of the SARS-CoV-2 Spike (S) protein on their surface. In this context, this work studies the effect of different rationally engineered mutations of the S protein to improve some of its characteristics. The studied variants harbored mutations such as proline substitutions for S stabilization, D614G from the early dominant pandemic form, the elimination of the S1/S2 furin cleavage site to improve S homogeneity, the suppression of a retention motif to favor its membrane localization, and cysteine substitutions to increase its immunogenicity and avoid potential undesired antibody-dependent enhancement (ADE) effects. The influence of the mutations on VLP expression was studied, as well as their immunogenic potential, by testing the recognition of the generated VLP variants by COVID-19 convalescent patients' sera. The results of this work are conceived to give insights on the selection of S protein candidates for their use as immunogens and to showcase the potential of VLPs as carriers for antigen presentation.

Distribution of cage size smooths the transition from diffusive to caging in microrheology

A colloidal particle undergoing Brownian motion and interacting with macromolecular structures embedded in complex fluids usually presents a diffusion regime at short times, with a diffusion coefficient related to the viscosity of the host solvent, and an intermediate regime where the mean squared displacement is found to be almost constant. This effect is attributed to the particle confinement in a cage formed by the surrounding complex fluid that hinders the motion of the tracer particle. An anomalous smooth transition that may span several decades usually characterises such a short-to-intermediate transition. In this work, this transition was studied using 1D, 2D, and 3D random walker simulations, finding that the origin of the smooth transition is a wide distribution of confining cages and the corresponding ensemble-averaged 3D mean squared displacement over all confined particles. The wider the cage distribution, the smoother the transition. Our results give the physical origin of the smooth transition, usually only discussed in terms of a distribution of relaxation times.

Crystal structure simulation of cationic cholesteric Liquid-Crystal polyesters. Non-Viral vectors

The crystal structure of six cationic cholesteric liquid crystal polyesters with choline, ammonium, and amide end groups are reported: PTOBEE-Choline [(C26H20O8)n-C5H13N]; PTOBDME-Choline [(C34H36O8)n-C5H13N]; PTOBEE-Ammonium [(C26H20O8)n-C5H13N]; PTOBDME-Ammonium [(C34H36O8)n-C5H13N]; PTOBEE-Amide (C26H19O9N)n and PTOBUME-Amide (C33H33O9N)n, have demonstrated non-viral vector properties in gene therapy by delivering DNA to the cell nucleus. They were synthesized by functionalization of precursor racemic polyesters via a condensation reaction between 4,4′-(terephthaloyl-di-oxydibenzoic chloride) (TOBC) and the racemic mixture of glycol (R-S-1,2-butanediol) or (R-S-1,2-dodecanediol), respectively. The resulting helical macromolecular structures exhibit unexpected optical activity and chiral morphology, previously attributed to the kinetic resolution of one preferable helical conformer of the possible four observed by NMR. Conformational analysis and energy minimization are performed on the four helical conformer monomeric models which are then polymerized to obtain helical chains. Three levels of chirality were observed in these polymer structures. Crystal models are built in triclinic primitive P1cells, with their molecular chains oriented parallel to the Z-axis (c lattice parameter equal to the helical pitch length), the cell parameters are obtained by performing geometry optimization tasks. A raw estimation of the relative weight of the four possible helical conformers on each synthetic cationic polymer has been performed on ideal mixtures considering their approximate known crystal structure with their experimental X-ray powder diffraction by Powder Quantitative Phase Analysis. The results would confirm the theory of the preferable recrystallization of a diastereoisomer, among the four possible, during the synthetic procedures. These findings shed light on the observed optical activity and provide insights into the crystal structure of these cationic polymers. The morphology of the cationic polymers is studied by ESEM in wet samples and crystals after drying the solvent.

Towards deciphering the source of rare earth elements in kerogen: Seawater or diagenetic porewater origin?

The composition of rare earth elements (REEs) in kerogen has long been hypothesized to preserve signatures of shallow seawater, assuming that kerogen primarily originates from biomass inhabiting the shallow photic zone. In this study, we examine the composition of REEs in kerogen samples obtained from the Ediacaran Dongkanshang and Fengtan sections in South China. Through a meticulous screening process, we provide evidence that kerogen samples exhibiting patterns of heavy rare earth element (HREE) enrichment are free from acid-insoluble mineral contamination and preserve unaltered REE signatures. This study reveals that kerogens display low total rare earth element (ΣREE) concentrations. As kerogen samples exhibit higher levels of HREE enrichment, the Ce/Ce*, Eu/Eu*, and Y/Ho ratios show an upward trend. Moreover, all the kerogen samples exhibit chondritic Y/Ho ratios (with an average of 32). These findings imply that REEs in kerogens are of diagenetic origin and influenced by the ambient redox conditions. Our results suggest that the distribution patterns of REEs in kerogens derive from porewater within methanogenic zones, as opposed to the oxic seawater photic zone. Consequently, the formation of kerogen could potentially occur within a similar domain as the methanogenic zones, where kerogens can assimilate REEs from pore fluids and incorporate them into their macromolecular structures.

Experimental phasing opportunities for macromolecular crystallography at very long wavelengths

Despite recent advances in cryo-electron microscopy and artificial intelligence-based model predictions, a significant fraction of structure determinations by macromolecular crystallography still requires experimental phasing, usually by means of single-wavelength anomalous diffraction (SAD) techniques. Most synchrotron beamlines provide highly brilliant beams of X-rays of between 0.7 and 2 Å wavelength. Use of longer wavelengths to access the absorption edges of biologically important lighter atoms such as calcium, potassium, chlorine, sulfur and phosphorus for native-SAD phasing is attractive but technically highly challenging. The long-wavelength beamline I23 at Diamond Light Source overcomes these limitations and extends the accessible wavelength range to λ = 5.9 Å. Here we report 22 macromolecular structures solved in this extended wavelength range, using anomalous scattering from a range of elements which demonstrate the routine feasibility of lighter atom phasing. We suggest that, in light of its advantages, long-wavelength crystallography is a compelling option for experimental phasing.

Self-supervised noise modeling and sparsity guided electron tomography volumetric image denoising

Cryo-Electron Tomography (cryo-ET) is a revolutionary technique for visualizing macromolecular structures in near-native states. However, the physical limitations of imaging instruments lead to cryo-ET volumetric images with very low Signal-to-Noise Ratio (SNR) with complex noise, which has a side effect on the downstream analysis of the characteristics of observed macromolecules. Additionally, existing methods for image denoising are difficult to be well generalized to the complex noise in cryo-ET volumes. In this work, we propose a self-supervised deep learning model for cryo-ET volumetric image denoising based on noise modeling and sparsity guidance (NMSG), achieved by learning the noise distribution in noisy cryo-ET volumes and introducing sparsity guidance to ensure smoothness. Firstly, a Generative Adversarial Network (GAN) is utilized to learn noise distribution in cryo-ET volumes and generate noisy volumes pair from single volume. Then, a new loss function is devised to both ensure the recovery of ultrastructure and local smoothness. Experiments are done on five real cryo-ET datasets and three simulated cryo-ET datasets. The comprehensive experimental results demonstrate that our method can perform reliable denoising by training on single noisy volume, achieving better results than state-of-the-art single volume-based methods and competitive with methods trained on large-scale datasets.

Cryo-Electron Microscopy for Cancer

A review paper on cryo-electron microscopy (cryo-EM) is essential to assess the recent advancements in this revolutionary imaging technique. As cryo-EM continues to revolutionize structural biology, a comprehensive review can consolidate the knowledge, highlight technical challenges, and offer insights into future developments, promoting better understanding and wider adoption of this cutting-edge technology. The absence of a review paper on the recent innovative approach of applying cryo-electron microscopy for cancer research hinders knowledge dissemination and impedes potential breakthroughs. A comprehensive review would bridge the gap, elucidating the successes and challenges of cryo-EM in cancer studies, fostering collaboration, and inspiring further investigations to combat cancer effectively.” Cryo-electron microscopy enables examining biomolecular structures at almost atomic precision while capturing multiple dynamic states more than 30 years after developing the industry's preferred method for cryo-embedding biological macromolecules under their native conditions. Techniques and equipment have significantly improved. Advanced image-processing methods are employed in research to facilitate the study of biological macromolecular structures and analyze their dynamics; for this, cryo-EM is a potent tool. Cryo-EM must effectively investigate the cellular macromolecular structure, including dynamic analysis using cutting-edge image-processing methods. Modern Analysis of individual particles using electron tomography is even more easily applicable to the procedure. With the development of single particle analysis and electron tomography, it is now more broadly applicable. These techniques have increased the method's applicability even further. Due to its ease of use and capacity to produce intricate and sophisticated data that can be used to comprehend biological structures better, cryo-EM is currently a more well-liked and available research tool. As a result, it serves as a useful tool for both academic and industrial research. Protein complexes, molecular pathways, and viral structures have also been studied using cryo-EM. Due to its adaptability, it has become a useful tool for illness and drug development. Its low cost and simplicity of usage have also made it a crucial research tool.

SBILib: a handle for protein modeling and engineering.

The SBILib Python library provides an integrated platform for the analysis of macromolecular structures and interactions. It combines simple 3D file parsing and workup methods with more advanced analytical tools. SBILib includes modules for macromolecular interactions, loops, super-secondary structures, and biological sequences, as well as wrappers for external tools with which to integrate their results and facilitate the comparative analysis of protein structures and their complexes. The library can handle macromolecular complexes formed by proteins and/or nucleic acid molecules (i.e. DNA and RNA). It is uniquely capable of parsing and calculating protein super-secondary structure and loop geometry. We have compiled a list of example scenarios which SBILib may be applied to and provided access to these within the library. SBILib is made available on Github at https://github.com/structuralbioinformatics/SBILib.

SARS-CoV-2 proteins structural studies using synchrotron radiation.

In the process of the development of structural biology, both the size and the complexity of the determined macromolecular structures have grown significantly. As a result, the range of application areas for the results of structural studies of biological macromolecules has expanded. Significant progress in the development of structural biology methods has been largely achieved through the use of synchrotron radiation. Modern sources of synchrotron radiation allow to conduct high-performance structural studies with high temporal and spatial resolution. Thus, modern techniques make it possible to obtain not only static structures, but also to study dynamic processes, which play a key role in understanding biological mechanisms. One of the key directions in the development of structural research is the drug design based on the structures of biomolecules. Synchrotron radiation offers insights into the three-dimensional time-resolved structure of individual viral proteins and their complexes at atomic resolution. The rapid and accurate determination of protein structures is crucial for understanding viral pathogenicity and designing targeted therapeutics. Through the application of experimental techniques, including X-ray crystallography and small-angle X-ray scattering (SAXS), it is possible to elucidate the structural details of SARS-CoV-2 virion containing 4 structural, 16 nonstructural proteins (nsp), and several accessory proteins. The most studied potential targets for vaccines and drugs are the structural spike (S) protein, which is responsible for entering the host cell, as well as nonstructural proteins essential for replication and transcription, such as main protease (Mpro), papain-like protease (PLpro), and RNA-dependent RNA polymerase (RdRp). This article provides a brief overview of structural analysis techniques, with focus on synchrotron radiation-based methods applied to the analysis of SARS-CoV-2 proteins.

Ghost authors revealed: The structure and function of human N6 -methyladenosine RNA methyltransferases.

Despite the discovery of modified nucleic acids nearly 75 years ago, their biological functions are still being elucidated. N6 -methyladenosine (m6 A) is the most abundant modification in eukaryotic messenger RNA (mRNA) and has also been detected in non-coding RNAs, including long non-coding RNA, ribosomal RNA, and small nuclear RNA. In general, m6 A marks can alter RNA secondary structure and initiate unique RNA-protein interactions that can alter splicing, mRNA turnover, and translation, just to name a few. Although m6 A marks in human RNAs have been known to exist since 1974, the structures and functions of methyltransferases responsible for writing m6 A marks have been established only recently. Thus far, there are four confirmed human methyltransferases that catalyze the transfer of a methyl group from S-adenosylmethionine (SAM) to the N6 position of adenosine, producing m6 A: methyltransferase-like protein (METTL) 3/METTL14 complex, METTL16, METTL5, and zinc-finger CCHC-domain-containing protein 4. Though the methyltransferases have unique RNA targets, all human m6 A RNA methyltransferases contain a Rossmann fold with a conserved SAM-binding pocket, suggesting that they utilize a similar catalytic mechanism for methyl transfer. For each of the human m6 A RNA methyltransferases, we present the biological functions and links to human disease, RNA targets, catalytic and kinetic mechanisms, and macromolecular structures. We also discuss m6 A marks in human viruses and parasites, assigning m6 A marks in the transcriptome to specific methyltransferases, small molecules targeting m6 A methyltransferases, and the enzymes responsible for hypermodified m6 A marks and their biological functions in humans. Understanding m6 A methyltransferases is a critical steppingstone toward establishing the m6 A epitranscriptome and more broadly the RNome. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

Visualizing macromolecular structures in situ by cryo-EM and cryo-ET

Visualizing macromolecular structures <i>in situ</i> by cryo-EM and cryo-ET

Predicted models and CCP4.

In late 2020, the results of CASP14, the 14th event in a series of competitions to assess the latest developments in computational protein structure-prediction methodology, revealed the giant leap forward that had been made by Google's Deepmind in tackling the prediction problem. The level of accuracy in their predictions was the first instance of a competitor achieving a global distance test score of better than 90 across all categories of difficulty. This achievement represents both a challenge and an opportunity for the field of experimental structural biology. For structure determination by macromolecular X-ray crystallography, access to highly accurate structure predictions is of great benefit, particularly when it comes to solving the phase problem. Here, details of new utilities and enhanced applications in the CCP4 suite, designed to allow users to exploit predicted models in determining macromolecular structures from X-ray diffraction data, are presented. The focus is mainly on applications that can be used to solve the phase problem through molecular replacement.

New Fluorescent Porphyrins with High Two-Photon Absorption Cross-Sections Designed for Oxygen-Sensitization: Impact of Changing the Connectors in the Peripheral Arms

In the continuation of our sustained interest in porphyrin-based dendrimers and their use as luminescent photosensitizers for two-photon photodynamic therapy (2P-PDT), we wondered about the effect of changing the connectors in our macromolecular structures. We also wanted to initiate preliminary studies on meso-tetraarylporphyrins decorated with more electron-releasing arms. Thus, various meso-tetrafluorenylporphyrin-cored star-shaped and dendrimeric derivatives have been synthesized and characterized, as well as their zinc(II) complexes. In the new dendrimeric derivatives, the peripheral fluorenyl units of the dendrons are linked to the inner core either by N-phenylcarbazole (CCbz) or triphenylamine (CTpa) connectors instead of the more classic 1,3,5-phenylene (CPh) linkers previously used by us. Selected linear and non-linear optical (LO and NLO) properties were then determined for these compounds via absorption or emission studies and by two-photon excited fluorescence (TPEF) measurements. It was found that the CCbz-containing dendrimer, which has the most rigid structure, exhibits a significantly lower two-photon absorption (2PA) cross-section than its CTpa analog, presenting a more flexible structure while rather similar luminescence and singlet oxygen activation quantum yields are found for both. The origin of this unexpected discrepancy is briefly discussed based on our photophysical data. It is then demonstrated that the latter dendrimer also outperforms several closely related dendrimers in terms of 2PA action cross-section and 2PA-oxygen sensitization, making its molecular architecture quite appealing for developing new 2PA photosensitizers suited to theranostic uses.

Role of Acids in Producing Ultrabright, Dual‐Emissive Carbon Dots and their Urea/Biuret Composites with Ultralong Afterglow

AbstractSince 2015, m‐phenylenediamines (mPD) have become a popular carbon source for the synthesis of carbonized polymer dots (CPDs). However, their exact fluorescence mechanism is still obscure. To elucidate this, inorganic acids that are carbon‐free are chosen as additives for a comparative study. It is found that the green fluorescence quantum yield (nearly 80%), photostability, and reaction yield (over 90%) can be enhanced by introduction of most of inorganic acids with moderate amount. Besides, green‐blue dual emission is observed in acid‐assisted groups. UV‐vis absorption, Fourier‐transform infrared spectroscopy, and surface‐enhanced Raman scattering results indicate that the green fluorescence center is composed of quinoid rings, whereas the blue fluorophore contains benzenoid rings. Moreover, room‐temperature afterglow with lifetime up to 1.25 s is observed exclusively in acid‐assisted CPDs composites with urea/biuret. The blue chromophore is proposed to be the origin of the triplet level that induces the long afterglow. This work provides an in‐depth understanding on the macromolecular structures of CPDs derived from phenylenediamines, and contributes a new line of thought to the origin of phosphorescence in N‐doped carbon dots.

Electropolymerization processing of side-chain engineered EDOT for high performance microelectrode arrays

Microelectrode Arrays (MEAs) are popular tools for in vitro extracellular recording. They are often optimized by surface engineering to improve affinity with neurons and guarantee higher recording quality and stability. Recently, PEDOT:PSS has been used to coat microelectrodes due to its good biocompatibility and low impedance, which enhances neural coupling. Herein, we investigate on electro-co-polymerization of EDOT with its triglymated derivative to control valence between monomer units and hydrophilic functions on a conducting polymer. Molecular packing, cation complexation, dopant stoichiometry are governed by the glycolation degree of the electro-active coating of the microelectrodes. Optimal monomer ratio allows fine-tuning the material hydrophilicity and biocompatibility without compromising the electrochemical impedance of microelectrodes nor their stability while interfaced with a neural cell culture. After incubation, sensing readout on the modified electrodes shows higher performances with respect to unmodified electropolymerized PEDOT, with higher signal-to-noise ratio (SNR) and higher spike counts on the same neural culture. Reported SNR values are superior to that of state-of-the-art PEDOT microelectrodes and close to that of state-of-the-art 3D microelectrodes, with a reduced fabrication complexity. Thanks to this versatile technique and its impact on the surface chemistry of the microelectrode, we show that electro-co-polymerization trades with many-compound properties to easily gather them into single macromolecular structures. Applied on sensor arrays, it holds great potential for the customization of neurosensors to adapt to environmental boundaries and to optimize extracted sensing features.