3D Structure Prediction Research Articles

Improving AlphaFold Predicted Contacts for Alpha-Helical Transmembrane Proteins Using Structural Features.

Residue contact maps provide a condensed two-dimensional representation of three-dimensional protein structures, serving as a foundational framework in structural modeling but also as an effective tool in their own right in identifying inter-helical binding sites and drawing insights about protein function. Treating contact maps primarily as an intermediate step for 3D structure prediction, contact prediction methods have limited themselves exclusively to sequential features. Now that AlphaFold2 predicts 3D structures with good accuracy in general, we examine (1) how well predicted 3D structures can be directly used for deciding residue contacts, and (2) whether features from 3D structures can be leveraged to further improve residue contact prediction. With a well-known benchmark dataset, we tested predicting inter-helical residue contact based on AlphaFold2's predicted structures, which gave an 83% average precision, already outperforming a sequential features-based state-of-the-art model. We then developed a procedure to extract features from atomic structure in the neighborhood of a residue pair, hypothesizing that these features will be useful in determining if the residue pair is in contact, provided the structure is decently accurate, such as predicted by AlphaFold2. Training on features generated from experimentally determined structures, we leveraged knowledge from known structures to significantly improve residue contact prediction, when testing using the same set of features but derived using AlphaFold2 structures. Our results demonstrate a remarkable improvement over AlphaFold2, achieving over 91.9% average precision for a held-out subset and over 89.5% average precision in cross-validation experiments.

Prediction of 3D RNA Structures from Sequence Using Energy Landscapes of RNA Dimers: Application to RNA Tetraloops.

Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dynamic and structurally complex properties of RNA, both experimental and traditional computational methods have difficulty in determining RNA's 3D structure. Herein, we introduce TAPERSS (Theoretical Analyses, Prediction, and Evaluation of RNA Structures from Sequence), a physics-based fragment assembly method for predicting 3D RNA structures from sequence. Using a fragment library created using discrete path sampling calculations of RNA dinucleoside monophosphates, TAPERSS can sample the physics-based energy landscapes of any RNA sequence with relatively low computational complexity. We have benchmarked TAPERSS on 21 RNA tetraloops, using a combinatorial algorithm as a proof-of-concept. We show that TAPERSS was successfully able to predict the apo-state structures of all 21 RNA hairpins, with 16 of those structures also having low predicted energies as well. We demonstrate that TAPERSS performs most accurately on GNRA-like tetraloops with mostly stacked loop-nucleotides, while having limited success with more dynamic UNCG and CUYG tetraloops, most likely due to the influence of the RNA force field used to create the fragment library. Moreover, we show that TAPERSS can successfully predict the majority of the experimental non-apo states, highlighting its potential in anticipating biologically significant yet unobserved states. This holds great promise for future applications in drug design and related studies. With discussed improvements and implementation of more efficient sampling algorithms, we believe TAPERSS may serve as a useful tool for a physics-based conformational sampling of large RNA structures.

Exploring the genetic diversity pattern of PvEBP/DBP2: A promising candidate for an effective Plasmodium vivax vaccine

Malaria remains a public health challenge. Since many control strategies have proven ineffective in eradicating this disease, new strategies are required, among which the design of a multivalent vaccine stands out. However, the effectiveness of this strategy has been hindered, among other reasons, by the genetic diversity observed in parasite antigens. In Plasmodium vivax, the Erythrocyte Binding Protein (PvEBP, also known as DBP2) is an alternate ligand to Duffy Binding Protein (DBP); given its structural resemblance to DBP, EBP/DBP2 is proposed as a promising antigen for inclusion in vaccine design. However, the extent of genetic diversity within the locus encoding this protein has not been comprehensively assessed. Thus, this study aimed to characterize the genetic diversity of the locus encoding the P. vivax EBP/DBP2 protein and to determine the evolutionary mechanisms modulating this diversity. Several intrapopulation genetic variation parameters were estimated using 36 gene sequences of PvEBP/DBP2 from Colombian P. vivax clinical isolates and 186 sequences available in databases. The study then evaluated the worldwide genetic structure and the evolutionary forces that may influence the observed patterns of genetic variation. It was found that the PvEBP/DBP2 gene exhibits one of the lowest levels of genetic diversity compared to other vaccine-candidate antigens. Four major haplotypes were shared worldwide. Analysis of the protein's 3D structure and epitope prediction identified five regions with potential antigenic properties. The results suggest that the PvEBP/DBP2 protein possesses ideal characteristics to be considered when designing a multivalent effective antimalarial vaccine against P. vivax.

Genome mining of Mycobacterium tuberculosis: targeting SufD as a novel drug candidate through in silico characterization and inhibitor screening.

Tuberculosis (TB) stands as the second most fatal infectious disease globally, causing 1.3 million deaths in 2022. The resurgence of TB and the alarming rise of antibiotic resistance demand urgent call to develop novel antituberculosis drugs. Despite concerted efforts to control TB, the disease persists and spreads rapidly on a global scale. Targeting stress response pathways in Mycobacterium tuberculosis (Mtb) has become imperative to achieve complete eradication. This study employs subtractive genomics to identify and prioritize potential drug targets among the hypothetical proteins of Mtb, focusing on indispensable pathways. Amongst 177 essential hypothetical proteins, 152 were nonhomologous to human. These proteins participated in 34 pathways, and a 20-fold enrichment of SUF pathway genes led to its selection as a target pathway. Fe-S clusters are fundamental, widely distributed protein cofactors involved in vital cellular processes. The survival of Mtb in a hypoxic environment relies on the iron-sulfur (Fe-S) cluster biogenesis pathway for the repair of damaged Fe-S clusters. It also protects pathogen against drugs, ensuring controlled iron utilization and contributing to drug resistance. In Mtb, six proteins of Fe-S cluster assembly pathway are encoded by the suf operon. The present study was focused on SufD because of its role in iron acquisition and prevention of Fenton reaction. The research further delves into the in silico characterization of SufD, utilizing bioinformatics tools for sequence and structure based analysis. The protein's structural features, including the identification of conserved regions, motifs, and 3D structure prediction enhanced functional annotation. Target based virtual screening of compounds from the ChEMBL database resulted in 12 inhibitors with best binding affinities. Drug likeness and ADMET profiling of potential inhibitors identified promising compounds with favorable drug-like properties. The study also involved cloning in SUMO-pRSF-Duet1 expression vector, overexpression, and purification of recombinant SufD from E. coli BL21 (DE3) cells. Optimization of expression conditions resulted in soluble production, and subsequent purification highlighting the efficacy of the SUMO fusion system for challenging Mtb proteins in E. coli. These findings provide valuable insights into pharmacological targets for future experimental studies, holding promise for the development of targeted therapy against Mtb.

State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction.

RNAs are essential molecules involved in numerous biological functions. Understanding RNA functions requires the knowledge of their 3D structures. Computational methods have been developed for over two decades to predict the 3D conformations from RNA sequences. These computational methods have been widely used and are usually categorised as either ab initio or template-based. The performances remain to be improved. Recently, the rise of deep learning has changed the sight of novel approaches. Deep learning methods are promising, but their adaptation to RNA 3D structure prediction remains difficult. In this paper, we give a brief review of the ab initio, template-based and novel deep learning approaches. We highlight the different available tools and provide a benchmark on nine methods using the RNA-Puzzles dataset. We provide an online dashboard that shows the predictions made by benchmarked methods, freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/state_of_the_rnart/.

Cellulolytic potential of mangrove bacteria Bacillus haynesii DS7010 and the effect of anthropogenic and environmental stressors on bacterial survivability and cellulose metabolism

Cellulose degrading bacterial diversity of Bhitarkanika mangrove ecosystem, India, was uncovered and the cellulose degradation mechanism in Bacillus haynesii DS7010 under the modifiers such as pH (pCO2), salinity and lead (Pb) was elucidated in the present study. The abundance of cellulose degrading heterotrophic bacteria was found to be higher in mangrove sediment than in water. The most potential strain, B. haynesii DS7010 showed the presence of endoglucanase, exoglucanase and β-glucosidase with the maximum degradation recorded at 48 h of incubation, with 1% substrate concentration at 41 °C incubation temperature. Two glycoside hydrolase genes, celA and celB were confirmed in this bacterium. 3D structure prediction of the translated CelA and CelB proteins showed maximum similarities with glycoside hydrolase 48 (GH48) and glycoside hydrolase 5 (GH5) respectively. Native PAGE followed by zymogram assay unveiled the presence of eight isoforms of cellulase ranged from 78 kDa to 245 kDa. Among the stressors, most adverse effect was observed under Pb stress at 1400 ppm concentration, followed by pH at pH 4. This was indicated by prolonged lag phase growth, higher reactive oxygen species (ROS) production, lower enzyme activity and downregulation of celA and celB gene expressions. Salinity augmented bacterial metabolism up to 3% NaCl concentration. Mangrove leaf litter degradation by B. haynesii DS7010 indicated a substantial reduction in cellulolytic potential of the bacterium in response to the synergistic effect of the stressors. Microcosm set up with the stressors exhibited 0.97% decrease in total carbon (C%) and 0.02% increase in total nitrogen (N%) after 35 d of degradation while under natural conditions, the reduction in C and the increase in N were 4.05% and 0.2%, respectively. The findings of the study suggest the cellulose degradation mechanism of a mangrove bacterium and its resilience to the future consequences of environmental pollution and climate change.

Structural basis for the preservation of a subset of topologically associating domains in interphase chromosomes upon cohesin depletion

Compartment formation in interphase chromosomes is a result of spatial segregation between euchromatin and heterochromatin on a few megabase pairs (Mbp) scale. On the sub-Mbp scales, topologically associating domains (TADs) appear as interacting domains along the diagonal in the ensemble averaged Hi-C contact map. Hi-C experiments showed that most of the TADs vanish upon deleting cohesin, while the compartment structure is maintained, and perhaps even enhanced. However, closer inspection of the data reveals that a non-negligible fraction of TADs is preserved (P-TADs) after cohesin loss. Imaging experiments show that, at the single-cell level, TAD-like structures are present even without cohesin. To provide a structural basis for these findings, we first used polymer simulations to show that certain TADs with epigenetic switches across their boundaries survive after depletion of loops. More importantly, the three-dimensional structures show that many of the P-TADs have sharp physical boundaries. Informed by the simulations, we analyzed the Hi-C maps (with and without cohesin) in mouse liver and human colorectal carcinoma cell lines, which affirmed that epigenetic switches and physical boundaries (calculated using the predicted 3D structures using the data-driven HIPPS method that uses Hi-C as the input) explain the origin of the P-TADs. Single-cell structures display TAD-like features in the absence of cohesin that are remarkably similar to the findings in imaging experiments. Some P-TADs, with physical boundaries, are relevant to the retention of enhancer–promoter/promoter–promoter interactions. Overall, our study shows that preservation of a subset of TADs upon removing cohesin is a robust phenomenon that is valid across multiple cell lines.

Structural basis for the preservation of a subset of topologically associating domains in interphase chromosomes upon cohesin depletion.

Compartment formation in interphase chromosomes is a result of spatial segregation between euchromatin and heterochromatin on a few megabase pairs (Mbp) scale. On the sub-Mbp scales, topologically associating domains (TADs) appear as interacting domains along the diagonal in the ensemble averaged Hi-C contact map. Hi-C experiments showed that most of the TADs vanish upon deleting cohesin, while the compartment structure is maintained, and perhaps even enhanced. However, closer inspection of the data reveals that a non-negligible fraction of TADs is preserved (P-TADs) after cohesin loss. Imaging experiments show that, at the single-cell level, TAD-like structures are present even without cohesin. To provide a structural basis for these findings, we first used polymer simulations to show that certain TADs with epigenetic switches across their boundaries survive after depletion of loops. More importantly, the three-dimensional structures show that many of the P-TADs have sharp physical boundaries. Informed by the simulations, we analyzed the Hi-C maps (with and without cohesin) in mouse liver and human colorectal carcinoma cell lines, which affirmed that epigenetic switches and physical boundaries (calculated using the predicted 3D structures using the data-driven HIPPS method that uses Hi-C as the input) explain the origin of the P-TADs. Single-cell structures display TAD-like features in the absence of cohesin that are remarkably similar to the findings in imaging experiments. Some P-TADs, with physical boundaries, are relevant to the retention of enhancer-promoter/promoter-promoter interactions. Overall, our study shows that preservation of a subset of TADs upon removing cohesin is a robust phenomenon that is valid across multiple cell lines.

An interactive visualization tool for educational outreach in protein contact map overlap analysis.

Recent advancements in contact map-based protein three-dimensional (3D) structure prediction have been driven by the evolution of deep learning algorithms. However, the gap in accessible software tools for novices in this domain remains a significant challenge. This study introduces GoFold, a novel, standalone graphical user interface (GUI) designed for beginners to perform contact map overlap (CMO) problems for better template selection. Unlike existing tools that cater more to research needs or assume foundational knowledge, GoFold offers an intuitive, user-friendly platform with comprehensive tutorials. It stands out in its ability to visually represent the CMO problem, allowing users to input proteins in various formats and explore the CMO problem. The educational value of GoFold is demonstrated through benchmarking against the state-of-the-art contact map overlap method, map_align, using two datasets: PSICOV and CAMEO. GoFold exhibits superior performance in terms of TM-score and Z-score metrics across diverse qualities of contact maps and target difficulties. Notably, GoFold runs efficiently on personal computers without any third-party dependencies, thereby making it accessible to the general public for promoting citizen science. The tool is freely available for download for macOS, Linux, and Windows.

Rational Design of TDP-43 Derived α-Helical Peptide Inhibitors: An In Silico Strategy to Prevent TDP-43 Aggregation in Neurodegenerative Disorders.

TDP-43, an essential RNA/DNA-binding protein, is central to the pathology of neurodegenerative diseases, such as amyotrophic lateral sclerosis and frontotemporal dementia. Pathological mislocalization and aggregation of TDP-43 disrupt RNA splicing, mRNA stability, and mRNA transport, thereby impairing neuronal function and survival. The formation of amyloid-like TDP-43 filaments is largely facilitated by the destabilization of an α-helical segment within the disordered C-terminal region. In this study, we hypothesized that preventing the destabilization of the α-helical domain could potentially halt the growth of these pathological filaments. To explore this, we utilized a range of in silico techniques to design and evaluate peptide-based therapeutics that bind to pathological TDP-43 amyloid-like filament crystal structures and resist β sheet conversion. Our computational approaches, including biophysical and secondary structure property prediction, molecular docking, 3D structure prediction, and molecular dynamics simulations, were used to assess the structure, stability, and binding affinity of these peptides in relation to pathological TDP-43 filaments. The results of our in silico analyses identified a selection of promising peptides which displayed a stable α-helical structure, exhibited an increased number of intramolecular hydrogen bonds within the helical domain, and demonstrated high binding affinities for pathological TDP-43 amyloid-like filaments. Molecular dynamics simulations provided further support for the structural and thermodynamic stability of these peptides, as they exhibited lower root-mean-square deviation and more favorable free energy landscapes over 300 ns. These findings establish α-helical propensity peptides as potential lead molecules for the development of novel therapeutics against TDP-43 aggregation. This structure-based computational approach for the rational design of peptide inhibitors opens a new direction in the search for effective interventions for ALS, FTD, and other related neurodegenerative diseases. The peptides identified as the most promising candidates in this study are currently subject to further testing and validation through both in vitro and in vivo experiments.

In Silico Prediction and Molecular Simulation of Antimicrobial Peptide Variants From Lactobacillus sp. Against Porphyromonas gingivalis and Fusobacterium nucleatum in Oral Squamous Cell Carcinoma

ABSTRACT Porphyromonas gingivalis and Fusobacterium nucleatum are known to contribute to a variety of tumorigenic pathways linked to the progression of oral squamous cell carcinoma (OSCC). The growing global incidence of antibiotic resistance highlights the critical need to consider the use of antimicrobial peptides (AMPs) as a viable alternative to conventional antibiotics. The current study comprehensively tested Lactobacillus sp.−derived AMPs against bacterially exacerbated OSCC. A total of 52 AMPs were obtained from various databases, and an in silico analysis determined their potent antibacterial and anticancer characteristics after a rigorous screening and pruning approach. Twelve AMPs were tested for 3D structural alignment prediction and validation, with the GH12 synthetic AMP serving as a control. These candidate peptides were thoroughly screened against six important virulence proteins of P. gingivalis and four of F. nucleatum, with the lowest energy score of the docked complexes measuring binding affinity and interactions with active residues being chosen. plpl_18 was determined as the most efficient new AMP that interacted with the virulence protein RagB of P. gingivalis and Fap2 of F. nucleatum with docking scores of −238.24 and −254.27 kcal/mol, respectively. This AMP plpl_18 was docked against selective target OSCC regulatory proteins such as cytokines, metallomatrix proteinase, MAPK, E‐cadherin, and JAK‐1 proteins. Among these proteins, it docked against matrix metalloproteinase‐9 with the highest negative docking scores of −7.5, −260.956, and −1361.9 kcal/mol using AutoDock Vina, HPEPDOCK, and ClusPro 2.0, respectively. Molecular dynamic simulation was used to perform extrapolated validation. These computational studies provide an essential foundation for anticipated laboratory and clinical investigations concerning the possibility of adapting therapeutic peptides based on probiotics to combat the proliferation of OSCC, which is accelerated by F. nucleatum and P. gingivalis.

Molecular Dynamics of Outer Membrane-Embedded Polysaccharide Secretion Porins Reveals Closed Resting-State Surface Gates Targetable by Virtual Fragment Screening for Drug Hotspot Identification.

Recent advances in iterative neural network analyses (e.g., AlphaFold2 and RoseTTA fold) have been revolutionary for protein 3D structure prediction, especially for difficult-to-manipulate α-helical/β-barrel integral membrane proteins. These model structures are calculated based on the coevolution of amino acids within the protein of interest and similarities to existing protein structures; the local effects of the membrane on folding and stability of the calculated model structures are not considered. We recently reported the discovery, 3D modeling, and characterization of 18-β-stranded outer-membrane (OM) WzpX, WzpS, and WzpB β-barrel secretion porins for the exopolysaccharide (EPS), major spore coat polysaccharide (MASC), and biosurfactant polysaccharide (BPS) pathways (respectively) in the Gram-negative social predatory bacterium Myxococcus xanthus DZ2. However, information was not obtained regarding the dynamic behavior of surface-gating WzpX/S/B loop domains or on potential treatments to inactivate these porins. Herein, we developed a molecular dynamics (MD) protocol to study the core stability and loop dynamism of neural network-based integral membrane protein structure models embedded in an asymmetric OM bilayer, using the M. xanthus WzpX, WzpS, and WzpB proteins as test candidates. This was accomplished through integration of the CHARMM-graphical user interface (GUI) and Molecular Operating Environment (MOE) workflows to allow for a rapid simulation system setup and facilitate data analysis. In addition to serving as a method of model structure validation, our molecular dynamics simulations revealed a minimal movement of extracellular WzpX/S/B loops in the absence of an external stimulus as well as druggable cavities between the loops. Virtual screening of a commercial fragment library against these cavities revealed putative fragment-binding hotspots on the cell-surface face of each β-barrel, along with key interacting residues, and identified promising hits for the design of potential binders capable of plugging the β-barrels and inhibiting polysaccharide secretion.

Molecular Prediction and Correlation of the Structure and Function of Universal Stress Protein A (UspA) from Salmonella Typhimurium.

Salmonella Typhimurium (ST) is a zoonotic pathogen that can cause gastroenteritis in humans when they consume contaminated food or water. When exposed to various stressors, both from living organisms (biotic) and the environment (abiotic), Salmonella Typhimurium produces Universal Stress Proteins (USPs). These proteins are gaining recognition for their crucial role in bacterial stress resistance and the ability to enter a prolonged state of growth arrest. Additionally, USPs exhibit diverse structures due to the fusion of the USP domain with different catalytic motifs, enabling them to participate in various reactions and cellular activities during stressful conditions. In this particular study, researchers cloned and analyzed the uspA gene obtained from poultry-derived strains of Salmonella Typhimurium. The gene comprises 435 base pairs, encoding a USP family protein consisting of 144 amino acids. Phylogenetic analysis demonstrated a close relationship between the uspA genes of Salmonella Typhimurium and those found in other bacterial species. Weused molecular dynamics simulations and 3D structure prediction to ensure thatthe USPA protein was stable.Furthermore, we alsocarried out motif search and network analysis of protein-protein interactions. The findings from this study offer valuable insights for the development of inhibitors targeted against Salmonella Typhimurium.

Genome-wide profiling of CBL interacting protein kinases (CIPKs) in banana unveils their role in abiotic stress signaling and stress tolerance enhancement

Calcineurin B-like (CBL)-interacting protein kinases (CIPKs) play a crucial role in the complex molecular systems of plants, acting as coordinators in different plant development processes, manage hormone signaling, and respond to environmental stress. Despite their well-established importance in various physiological process, the CIPK gene family in banana has remained an unexplored, creating a knowledge gap that this research aims to address. Through comprehensive analysis, we identified and characterized 34 CIPK genes in the banana genome. Structural diversity analysis revealed distinct clades characterized by homologs with varying intron-rich and intron-poor members. Particularly, evolutionary conservation was observed in intron-less members. Exploring conserved motifs and physicochemical properties provided insights into the detailed structure of specific homologs. Subcellular localization predictions indicated that most CIPK proteins primarily reside in the cytoplasm and nucleus, indicating their involvement in essential cellular activities. The predicted chromosomal distribution pattern suggests that the CIPK gene family largely expanded through segmental duplications in the banana genome. The phylogenetic analysis has uncovered two monophyletic clades for intron-less and intron-containing homologs, providing insights into two separate evolutionary history and expansion of the CIPK gene family. The clustering pattern in the phylogeny suggest that sequence of CIPKs members in the different genomes are largely conserved during course of evolution after evolution of higher plants. However, a few members having diverse sequences that these homologs are evolving with higher rate of evolution. Diverse physiological roles of banana CIPKs members are further supported by the presence of numerous cis-acting elements associated with growth, development, hormone responsiveness, and stress, with certain MaCIPK promoters exhibiting multiple hormone-sensitive elements. The protein-protein interaction network sheds light on the functional associations between MaCIPKs and MaCBLs, highlighting specific clusters of interactions. 3D protein structure predictions provide additional insights, revealing variations among the MaCIPK proteins. Real-time expression analysis across various tissues and developmental stages emphasizes the significant roles played by CIPKs in plant development. Under stress conditions, 16 in leaves and 22 in root MaCIPK gene transcript levels were up –regulated in response to drought and cold stress. These insights into the dynamic expression and functional roles of CIPK genes provide valuable information for understanding the mechanisms underlying banana's resilience to stress, with direct implications for the development of stress-resistant banana varieties. Overall this research offers promising prospects for enhancing global food security by ensuring the sustainability of banana production in the face of changing environmental conditions.

De novo prediction of RNA 3D structures with deep generative models.

We present a Deep Learning approach to predict 3D folding structures of RNAs from their nucleic acid sequence. Our approach combines an autoregressive Deep Generative Model, Monte Carlo Tree Search, and a score model to find and rank the most likely folding structures for a given RNA sequence. We show that RNA de novo structure prediction by deep learning is possible at atom resolution, despite the low number of experimentally measured structures that can be used for training. We confirm the predictive power of our approach by achieving competitive results in a retrospective evaluation of the RNA-Puzzles prediction challenges, without using structural contact information from multiple sequence alignments or additional data from chemical probing experiments. Blind predictions for recent RNA-Puzzle challenges under the name "Dfold" further support the competitive performance of our approach.

In Silico Exploration of APOE4 Inhibitors: Molecular Docking and ADMET Profiling for Alzheimer's Therapy

Background: Alzheimer's disease (AD), a prevalent neurodegenerative disorder, poses significant challenges to healthcare systems worldwide. The aggregation of Amyloid-β peptide and the presence of apolipoprotein E4 (apoE4) are recognized as central factors in AD pathogenesis. Despite the availability of FDA-approved drugs, there remains a dire need for more effective and targeted treatments to combat this disease. The advent of in silico methodologies offers a promising avenue for accelerating the drug discovery process, providing a cost-effective and efficient means to screen for potential therapeutic agents. Objective: This study aimed to identify and evaluate potential inhibitors of apoE4 using in silico drug discovery methods. By targeting the interaction domain of apoE4, the study sought to discover compounds capable of reducing beta-amyloid aggregation, thus offering a novel approach to AD therapy. Methods: Computational techniques, including the Lipinski Rule of Five and ADMET filtering, were employed to screen a dataset of 80 chemical inhibitors retrieved from the NCBI database. Molecular docking was performed to assess the binding affinities and orientations of the compounds to apoE4, utilizing Phyre2 for 3D structure prediction and AutoDock Vina for docking simulations. The selection criteria for lead compounds were based on their binding energy, hydrogen bonding interactions, and compliance with drug-like properties. Results: Out of the initial 80 compounds screened, 15 were excluded based on the Lipinski Rule of Five, and an additional 43 were deemed unsuitable following ADMET analysis. Molecular docking identified two compounds, taxifolin and luteolin, exhibiting superior binding affinities to apoE4, with taxifolin showing the lowest binding energy of -7.4 kcal/mol and the highest number of hydrogen bonds in one of its conformations. Conclusion: The study successfully leveraged in silico methods to identify taxifolin and luteolin as potent inhibitors of apoE4, highlighting the potential of computational drug discovery in developing novel AD therapies. These findings pave the way for future experimental validation and clinical trials to assess the therapeutic efficacy and safety of these compounds in treating Alzheimer's disease.

E and M SARS-CoV-2 membrane protein expression and enrichment with plant lipid droplets.

Plants are gaining traction as a cost-effective and scalable platform for producing recombinant proteins. However, expressing integral membrane proteins in plants is challenging due to their hydrophobic nature. In our study, we used transient and stable expression systems in Nicotiana benthamiana and Camelina sativa respectively to express SARS-CoV-2 E and M integral proteins, and target them to lipid droplets (LDs). LDs offer an ideal environment for folding hydrophobic proteins and aid in their purification through flotation. We tested various protein fusions with different linkers and tags and used three dimensional structure predictions to assess their effects. E and M mostly localized in the ER in N. benthamiana leaves but E could be targeted to LDs in oil accumulating tobacco when fused with oleosin, a LD integral protein. In Camelina sativa seeds, E and M were however found associated with purified LDs. By enhancing the accumulation of E and M within LDs through oleosin, we enriched these proteins in the purified floating fraction. This strategy provides an alternative approach for efficiently producing and purifying hydrophobic pharmaceuticals and vaccines using plant systems.

Discovery of a trefoil knot in the RydC RNA: Challenging previous notions of RNA topology

Knots are very common in polymers, including DNA and protein molecules. Yet, no genuine knot has been identified in natural RNA molecules to date. Upon re-examining experimentally determined RNA 3D structures, we discovered a trefoil knot 31, the most basic non-trivial knot, in the RydC RNA. This knotted RNA is a member of a small family of short bacterial RNAs, whose secondary structure is characterized by an H-type pseudoknot. Molecular dynamics simulations suggest a folding pathway of the RydC RNA that starts with a native twisted loop. Based on sequence analyses and computational RNA 3D structure predictions, we postulate that this trefoil knot is a conserved feature of all RydC-related RNAs. The first discovery of a knot in a natural RNA molecule introduces a novel perspective on RNA 3D structure formation and on fundamental research on the relationship between function and spatial structure of biopolymers.

RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality.

RNA is a complex macromolecule that plays central roles in the cell. While it is well known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.

Delving into the significance of the His289Tyr single-nucleotide polymorphism in the glutamate ionotropic receptor kainate-1 (Grik1) gene of a genetically audiogenic seizure model.

Genetic abnormalities affecting glutamate receptors are central to excitatory overload-driven neuronal mechanisms that culminate in seizures, making them pivotal targets in epilepsy research. Increasingly used to advance this field, the genetically audiogenic seizure hamster from Salamanca (GASH/Sal) exhibits generalized seizures triggered by high-intensity acoustic stimulation and harbors significant genetic variants recently identified through whole-exome sequencing. Here, we addressed the influence of the missense single-nucleotide polymorphism (C9586732T, p.His289Tyr) in the glutamate receptor ionotropic kainate-1 (Grik1) gene and its implications for the GASH/Sal seizure susceptibility. Using a protein 3D structure prediction, we showed a potential effect of this sequence variation, located in the amino-terminal domain, on the stability and/or conformation of the kainate receptor subunit-1 protein (GluK1). We further employed a multi-technique approach, encompassing gene expression analysis (RT-qPCR), Western blotting, and immunohistochemistry in bright-field and confocal fluorescence microscopy, to investigate critical seizure-associated brain regions in GASH/Sal animals under seizure-free conditions compared to matched wild-type controls. We detected disruptions in the transcriptional profile of the Grik1 gene within the audiogenic seizure-associated neuronal network. Alterations in GluK1 protein levels were also observed in various brain structures, accompanied by an unexpected lower molecular weight band in the inferior and superior colliculi. This correlated with substantial disparities in GluK1-immunolabeling distribution across multiple brain regions, including the cerebellum, hippocampus, subdivisions of the inferior and superior colliculi, and the prefrontal cortex. Notably, the diffuse immunolabeling accumulated within perikarya, axonal fibers and terminals, exhibiting a prominent concentration in proximity to the cell nucleus. This suggests potential disturbances in the GluK1-trafficking mechanism, which could subsequently affect glutamate synaptic transmission. Overall, our study sheds light on the genetic underpinnings of seizures and underscores the importance of investigating the molecular mechanisms behind synaptic dysfunction in epileptic neural networks, laying a crucial foundation for future research and therapeutic strategies targeting GluK1-containing kainate receptors.