Search Results (355)

Severe traumatic brain injury (TBI) is one of the leading causes of mortality and disability worldwide. To date, there are no clinically effective neuroprotective agents. Biomaterials that combine structural support for damaged tissue with a depot for therapeutic agents may represent a key solution to this problem. To evaluate the neuroprotective potential of a collagen matrix derived from the jellyfish Rhizostoma pulmo (R. pulmo) and modified with sodium thiosulfate (Na₂S₂O₃) as an hydrogen sulfide (H₂S) donor in a bioengineered platform for the treatment of severe TBI. Comprehensive characterization of the collagen matrix (electrophoresis, fluorescence microscopy), its implantation in a mouse model of severe TBI, and subsequent morphological, histological, ultrastructural, and immunohistochemical analyses of connexin 43 (Cx43) and p53 protein (p53) were performed. In addition, molecular dynamics simulations of the interactions between sulfur-containing compounds and target proteins were conducted. The effects were compared with inhibition of endogenous H₂S synthesis using aminooxyacetic acid (AOAA). The collagen matrix retains the properties of type I collagen and forms a three-dimensional porous structure with high hydrophilicity and biocompatibility. Implantation ensures effective defect filling, reduces cystic degeneration, and preserves cortical structure. Modification with Na₂S₂O₃ results in a significant reduction in both nuclear and cytoplasmic accumulation of p53, prevention of Cx43 dysregulation, a decrease in the proportion of damaged neurons and inflammatory infiltration, and preservation of tissue ultrastructure. In contrast, inhibition of CBS with AOAA exacerbates pathological changes. Molecular modeling demonstrated that S₂O₃²⁻ is capable of forming stable electrostatic interactions with domains of p53 and Cx43 under conditions of acidosis and elevated Ca²⁺. A collagen matrix derived from R. pulmo and modified with Na₂S₂O₃ represents a promising biodegradable platform that combines structural support with local H₂S-dependent regulation of key mechanisms of secondary brain injury. This approach provides a multilevel neuroprotective effect and opens new opportunities for the development of therapeutic implants for severe TBI. Full article

Enterovirus B (EVB) is the most prevalent species of human enteroviruses, responsible for a wide range of diseases, including hand, foot, and mouth disease, viral meningitis, myocarditis, and neonatal sepsis, imposing a significant disease burden primarily on children. Coxsackievirus B (CVB1-6) and various echovirus (E) serotypes are the major serotypes of EVB. Since no antiviral drug or vaccine is available, it is important to strengthen monitoring, risk assessment, and early warning of genomic variations for EVB. CVB1, CVB3, E6, and E30 were selected as representative EVB serotypes for this study due to the availability of three-dimensional structures and their global prevalence. To evaluate the mutation effects of structural proteins on structural stability and receptor-binding affinity, computational saturation mutagenesis of EVB serotypes was performed using FoldX. Furthermore, based on data from deep mutational scanning for CVB3, a risk prediction model for EVB fitness was constructed by machine learning algorithms and applied to other EVB serotypes. Finally, we integrated three phenotypes—structural stability, receptor-binding affinity and fitness—to evaluate genomic variation risk of EVB and tracked the prevalence of high-risk mutants in natural viral sequences through molecular evolution analysis and mutation profiles. We identified the N-terminus and C-terminus of VP1 and the EF loop of VP2 as the EVB regions of highest genomic variation risk, and high-risk mutations had played significant roles in viral evolutionary history. These findings provide a framework for multi-phenotypic and multi-data approaches to viral risk assessment and offer insights to support the development of antiviral drugs and vaccines. Full article

Uncontrolled bleeding, coagulation disorders, and infection-related complications still present substantial challenges in emergency medicine and trauma care. Developing multifunctional hemostatic materials represent an effective strategy for addressing clinical hemostasis problems. In this study, Galla chinensis polyphenols, the effective extract of Galla chinensis, were loaded onto calcium alginate-mesoporous silica granules (CMS-GC). The CMS granules were prepared by in situ liquid-phase technology and GC was loaded by impregnation methods. In vitro and in vivo studies showed that CMS-GC not only activate the endogenous coagulation pathway via GC, but also the multi-level interconnected pores of CMS granules can promote the cross-linking of GC with plasma proteins and formation of a three-dimensional network structure, which further enhances the coagulation effect and shortens the blood clotting time to less than 80 s. In rat liver and femoral artery hemorrhage models, CMS-GC significantly shortened hemostasis time and reduced blood loss, demonstrating superior hemostatic performance. Moreover, within the moist environment sustained by alginate, GC mitigates inflammatory responses via its antibacterial and free-radical clearance properties, and synergistically facilitates wound healing. This CMS-GC multifunctional granule provides an efficient new strategy for traumatic bleeding and subsequent repair. Full article

Medium-resolution cryo-electron microscopy (cryo-EM) density maps preserve substantial information about protein secondary-structure organization; however, accurately recovering the topology and connectivity of α-helices and β-strands remains challenging due to noise, structural heterogeneity, and the intrinsic resolution limitations that obscure residue-level detail. Topology determination is a key intermediate step toward building atomic protein models from medium-resolution cryo-EM density maps. It requires identifying the correct correspondence and orientation between secondary-structure elements (SSEs), i.e., α-helices and β-strands, predicted from the amino-acid sequence and those detected in the three dimensional (3D) density map. Despite significant advances in cryo-EM reconstruction and molecular modelling, this correspondence problem remains a challenging task, particularly in the presence of noisy density maps and in large, topologically complex α/β proteins. To address this issue, we propose a fully automated, classification-based framework that infers protein secondary-structure topology directly from medium-resolution cryo-EM density maps. Specifically, we cast topology determination as a supervised classification problem in three-dimensional space, leveraging geometric learning on model-derived Cα coordinate representations to establish SSE correspondences, and a Dynamic Time Warping (DTW)-based procedure to resolve density-stick directionality. Validation on a benchmark of 38 proteins spanning both simulated and experimental cryo-EM maps and covering diverse fold classes (α, β, and α/β) demonstrates strong and consistent performance. Among the evaluated predictors, the Voronoi (1-NN) classifier achieves the highest average correspondence quality, with a mean F1-score of 96.82% across the full benchmark. The framework also scales to large, topologically dense targets containing up to 65 secondary-structure elements while preserving very fast correspondence inference (<3 ms), offering a substantial improvement over prior baselines in both accuracy and computational cost. Overall, the classification-driven strategy provides reliable SSE-to-density matching and, when coupled with DTW-based direction selection, yields stronger topology constraints that directly support model building and refinement from medium-resolution cryo-EM reconstructions, while remaining easy to integrate into existing structural interpretation pipelines. Full article

Background/Objectives: Retinoblastoma treatment remains limited by therapeutic resistance and toxicity. While melphalan is a key chemotherapeutic agent, its efficacy is constrained by adverse effects. Curcumin has emerged as a potential adjunct owing to its capacity to regulate oxidative stress and oncogenic signaling pathways, including STAT3. This study aimed to assess the synergistic tumor-inhibitory effects of melphalan–curcumin combined treatment and to investigate the roles of ROS, apoptosis, and STAT3-associated signaling, including validation in a three-dimensional (3D) tumor spheroid model. Materials and Methods: Human retinoblastoma (WERI-Rb-1) and normal keratinocyte (HaCaT) cells were exposed to melphalan, curcumin and the combined treatment regimen. Cell viability was analyzed by MTT assay, and drug interactions were analyzed using the Chou–Talalay method. Migration was evaluated by scratch assay. Intracellular ROS levels were quantified using the DCFH-DA assay and confirmed by flow cytometry. Apoptosis was quantified by Annexin V/PI staining, and caspase activity was assessed colorimetrically and by immunocytochemistry. Cytokine levels were determined by ELISA, and gene expression profiling of STAT3 and apoptosis-associated genes were performed using qRT-PCR. Three-dimensional tumor spheroids were established to evaluate treatment responses in a physiologically relevant model. The contribution of ROS was further investigated using N-acetyl-L-cysteine (NAC) pretreatment. Results: The combination of melphalan and curcumin notably reduced WERI-Rb-1 cell viability in a synergistic manner (CI < 1) while exhibiting lower cytotoxicity in HaCaT cells, indicating selective antitumor activity. Co-treatment markedly inhibited cell migration and increased intracellular ROS levels. Cells pretreated with NAC significantly reduced ROS levels accumulation and moderately restored cellular viability, supporting a contributory role of oxidative stress. The combination treatment induced pronounced apoptosis, with increased early and late apoptotic cell populations, enhanced caspase-7 and caspase-9 activity, and elevated caspase-9 protein expression. These effects were associated with upregulation of pro-apoptotic genes (BAX, CASP3, CASP7, CASP9), downregulation of anti-apoptotic genes (BCL2, SURVIVIN), and reduction in STAT3 mRNA expression. In addition, the combination reduced pro-inflammatory cytokine levels. Importantly, these effects were recapitulated in 3D tumor spheroids, where the combination treatment reduced spheroid size and viability and induced structural disruption. NAC-mediated rescue experiments in 3D models further supported the notion that ROS contributes to, but is not solely responsible for, the observed effects. Conclusions: Overall, these results suggest that melphalan and curcumin exert synergistic and selective antitumor effects in retinoblastoma cells, associated with changes consistent with ROS-related effects, mitochondrial apoptotic processes, and STAT3-related transcriptional alterations rather than definitive pathway activation. The validation of these effects in a 3D tumor spheroid model provides additional support for the potential clinical significance of this combined treatment; however, additional protein-level and functional validation is required. Full article

Deep learning has transformed protein design from a field long dominated by explicit energy-function optimization into one dominated by probabilistic generative modeling. In this review, we summarize the protein representation algorithmic basis for this transition, from sequence-centered encodings to geometric graph representations and, more recently, SE(3)-equivariant structural manifolds that directly respect three-dimensional symmetry. We classify current approaches into three methodological paradigms according to how sequence and structure are related during design: sequence–structure decoupled design, hybrid approaches, and sequence–structure co-design. For decoupled workflows, we discuss hallucination, backbone generation, and backbone-conditioned sequence design. For hybrid approaches, we examine integrated two-stage architectures and predictor-driven iterative co-refinement. For co-design, we review explicit joint generative formulations in which sequence and structure are treated as a coupled design state throughout generation. Additionally, we summarize evaluation principles for assessing the design results, such as physical validity, folding consistency, and design coverage, and then introduce some important applications in several fields. Taken together, these developments indicate that generative protein design is making progress from structure generation toward the programmable engineering of complex biological function. Full article

One of the most puzzling and unsolved challenges in molecular biology is understanding how proteins fold. Despite having advanced predictive tools that can accurately estimate the native structures of proteins, we still lack a comprehensive model that explains how amino acid sequences dictate folding pathways and trajectories. This manuscript introduces a novel treatment for the issue by employing the “principle of least action.” This approach enables us to explore an intriguing question: how does a protein achieve its native state at a constant folding rate and within a biologically plausible time frame? A response to this inquiry will help us understand why proteins must fold along specific pathways and identify the boundary conditions that limit their availability. Furthermore, the principle of least action—together with the effective trajectory conjecture—enables us to explain why different proteins could exhibit the same folding rate. Finally, it will enable us to provide an in-depth description of the genesis and solution of Levinthal’s paradox. Our results are expected to pave the way for a more profound understanding of how proteins fold, shedding light on how the amino acid sequence and its surrounding environment encode the protein’s folding pathways and, consequently, the protein’s three-dimensional structure. Full article

For a protein to perform its biological functions, it must adopt a specific three-dimensional conformation. In addition, many proteins require the assistance of other protein complexes known as chaperonins to fold —i.e., to acquire such a specific conformation—, although the exact mechanisms whereby the chaperonins act and assist the folding process have not been completely determined. In this work, we characterize the physical environment at the interior of the chaperonin HSP60/HSP10 via Molecular Dynamics Simulations. We found that, inside the cavity of the chaperonin (within a region covering much of the cavity’s volume), the long-range electrostatic potential presents a structured pattern that, except for small fluctuations, does not change in time. The electrostatic potential generates an electric field that can be modeled, as a first approximation, as constant and unidirectional $(\mathit{E}/\left(\mathrm{V}·{\AA }^{-1}\right)\approx -0.0054\widehat{𝚤}+0.010\widehat{\mathit{𝚥}}-0.162\widehat{k}$, here the chaperonin’s main axis is aligned along $\widehat{k})$, which can produce large deformations in the structure of a heated protein (Rhodanese); the long-range approximated $\mathit{E}\left(\mathit{r}\right)$ can in fact unfold the Rhodanese, when applied as an external field. Finally, we discuss the possible implications of such an electric field for the protein folding problem, within the context of proteins whose folding is assisted by chaperones. The existence and effects of the electric field are consistent with several theories and experimental observations related to the protein folding problem, in particular with the foldon view. Full article

Seasonal influenza A evolves quickly through mutations in haemagglutinin (HA) and neuraminidase (NA), which can reduce vaccine match and lower protection. Many sequence-only models do not link codon-level mutations to three-dimensional (3D) protein context and long-term evolutionary signals within one scoring framework. This study presents TRIAD-Influenza (TRIAD: Token–Residue–Integrated Architecture for Drift), a multi-view transformer that combines (i) codon- and residue-level sequence representations, (ii) structure-derived residue interaction features from predicted HA/NA models, and (iii) an embedding-space phylogeny that captures cluster and drift context. The pipeline curates more than $3\times {10}^5$ paired HA/NA coding sequences from the NCBI Virus resource (2010–2024) using strict quality control and codon-aware alignment and predicts 3D structures for nearly all unique HA and NA proteins to build contact graphs and surface/stability descriptors. TRIAD-Influenza outputs a continuous, structure-aware risk score for each HA/NA pair and produces interpretable mutation hotspot maps using gradient saliency and a contact-weighted mutation risk index (CMRI). On rolling-origin temporal cross-validation and for a temporally held-out internal test window with strong class imbalance (∼3.4% high-risk), the model shows strong ranking performance (AUROC $\approx 0.89$; AUPRC $\approx 0.44$; Brier score $=0.069$) while operating at surveillance speed (median latency $\approx 1.6$ ms per HA/NA pair). External validation on independent GISAID/Nextstrain cohorts (2023–2024; 5000 isolates) preserves discrimination (AUROC $\approx 0.85$–$0.86$). Predicted risk scores correlate with experimental haemagglutination inhibition (HI) antigenic distances (Spearman $\rho$ up to ≈0.82 at the virus-aggregated level), and CMRI hotspots enrich known epitope and deep mutational scanning escape residues (odds ratios $\approx 2.7$–$3.6$). Overall, token–residue–phylogeny coupling enables rapid, structure-aware prioritisation of emerging influenza A HA/NA sequences and delivers compact hotspot maps for expert review and targeted experiments. Full article

Protein kinase inhibition can be achieved through various mechanisms, including blocking phosphorylation activity or disrupting regulatory interactions. While small molecule inhibitors have shown promise, their selectivity remains challenging due to the structural similarities among kinase catalytic sites. To design selective kinase inhibitors based on peptide terminal tail interactions with the activation segment, focusing on five kinases with different conformational states: GSK3, PAK4, TTN (OUT conformation) and PKB, FLT3 (IN conformation). Three-dimensional structures from RCSB PDB were optimized using MODELLER version 9.0. Peptide sequences were designed with PeptiDerive (Rosetta) and RosettaDesign version 3.5, followed by pharmacophore modeling based on key interaction residues. Virtual screening was then conducted with PyRx 0.8 and molecular docking with AutoDock Vina 1.1.2. Molecular dynamics simulations were performed using Desmond v6.6 (Schrödinger Suite 2016, Multisim v3.8.5.19) (100 ns, NPT ensemble, 300 K). Analysis of the five kinases revealed distinct interaction profiles with designed peptidomimetic compounds. Kinases displaying the IN conformation of the activation segment (PKB and FLT3) consistently showed superior stability and stronger interaction profiles compared to those in the OUT conformation. The designed compounds formed key hydrogen bonds and hydrophobic interactions with critical residues in the activation segment binding pocket. The most promising inhibitors demonstrated stability throughout the molecular dynamics simulations, with IN conformation kinases maintaining more consistent conformational profiles than their OUT conformation counterparts. Kinases with IN conformation of the activation segment demonstrated superior stability and interaction profiles compared to OUT conformations. These findings contribute to our understanding of selective kinase inhibition and provide a framework for developing novel inhibitors, particularly for PKB and FLT3. The implications of this study extend to rational drug design approaches that leverage natural regulatory mechanisms for therapeutic intervention, though further optimization is needed for GSK-3β, PAK4, and TTN to improve stability and binding affinity. Full article

Accurate prediction of protein sidechain conformations is a fundamental challenge in structural biology, with diverse applications ranging from protein structure determination to computational drug design. The performance of backbone-dependent rotamer libraries is often limited by discrete binning artifacts and difficulties handling sparse conformational regions. In this work, we present a Bayesian framework for rotamer prediction that addresses these limitations through Dirichlet priors and spatial smoothing. Our approach models rotamer probabilities as continuous functions of backbone dihedral angles, using circular Gaussian convolution, to make the most of statistical strength from neighboring conformations while respecting the periodic nature of angular data. We constructed rotamer libraries through structural clustering of sidechain conformations rather than chi angle binning, ensuring that each rotamer represents a distinct three-dimensional geometry. We evaluated and compared our framework against the state-of-the-art libraries on two independent test sets. Our Dirichlet model achieved chi angle prediction accuracy of 59–60%. Notably, our method produced consistently lower angular errors, an approximate 13% reduction in mean deviation, suggesting that the continuous probability distributions better capture subtle conformational preferences. Further, we explored the incorporation of non-sequential context by including the identity of nearby non-neighboring residues as an example of extensibility of our framework. Full article

The order Herpesvirales contains three families, Orthoherpesviridae, Alloherpesviridae, and Malacoherpesviridae. The time since divergence of families from the common ancestor makes protein primary sequence comparison an insensitive tool for identifying common genes. Comparison of three-dimensional protein structures can reveal similarities that are not evident in primary sequences. Salmonid herpesvirus 1 (SalHV-1) is an alloherpesvirus. Complete sequencing of SalHV-1 VR-868 strain Winthrop by a combination of short- and long-read methods revealed 120 putative open reading frames (ORFs). BLAST search for similar protein sequences discovered five ORFs that encoded proteins with homologs in the orthoherpesviruses, including the major capsid protein, capsid triplex subunit 2, the catalytic subunit of the DNA polymerase, the helicase subunit of the helicase/primase complex, and the terminase ATPase subunit. An annotation of the ORFs of SalHV-1 was performed in which ORFs of SalHV-1 were modeled using AlphaFold3, and the models were used as prompts for structural similarity search using DALI and FoldSeek. Completion of this search strategy for the entire genome expanded the set of genes shared among the Herpesvirales to include additional proteins related to DNA replication and genome integrity, capsid assembly and genome packaging, and capsid nuclear egress. No homologs for any tegument proteins or proteins of the conserved entry apparatus of the Herpesviridae (gB, gH or gL) were discovered. Full article

Multiple viruses suppress the antiviral defense system of the host for optimal growth and pathogenesis by co-opting the ubiquitin-mediated proteasomal system (UPS) that promotes the degradation of cellular substrates belonging to the interferon pathway. In the Orthopneumovirus genus, respiratory syncytial virus (RSV), a significant pathogen in human and other animals, employs a pair of viral nonstructural proteins (NS1, NS2) to assemble the UPS. The lack of experimental three-dimensional structures of the substrate proteins and the NS-assembled UPS has impeded progress in our understanding of the mechanism of this assembly process. In an effort to remedy this deficiency, I have taken advantage of the burgeoning field of AI (artificial intelligence) and machine learning programs, such as AlphaFold3, to model the pre-ubiquitylation cores in various combination of the subunits to construct three-dimensional structures, named ‘computed structure models’ (CSMs). The UPS core universally comprises an adapter protein connected to the “substrate” that is to be degraded by the “substrate receptor”. The NS proteins are believed to act as receptors, and cellular Elongin BC as an adapter. These CSMs lend support to the biochemical results where known while also suggesting that the complete core of three proteins is energetically more stable than a complex of only the NS protein and the substrate. In the absence of experimental structures, these results offer, for the first time, a mechanistic insight into RSV-triggered assembly of the UPS, which should allow for a better design of future experiments, and eventually new antiviral regimens. Full article

Background/Objective: Pancreatic and duodenal homeobox 1 (PDX1) is a key transcription factor required for pancreatic development and maintenance of β-cell function. Genetic variants in PDX1 have been associated with monogenic forms of diabetes, including maturity-onset diabetes of the young type 4 (MODY4). However, the func-tional consequences of many reported non-synonymous single-nucleotide polymorphisms (nsSNPs) in PDX1 remain unclear. In this study, an integrated in silico approach was applied to systematically identify and characterize po-tentially deleterious nsSNPs in the PDX1 gene. Methods: Missense variants were retrieved from public databases and evaluated using multiple sequence- and structure-based prediction tools to assess functional impact, disease association, protein stability, and structural consequences. Variants considered deleterious were further examined through three-dimensional structural modeling and molecular dynamics simulation. Results: Several nsSNPs were identified with consistent predictions of pathogenicity, reduced protein stability, and pronounced structural and dynamic perturbations. Variants including R197G, Y170N, and T151K in the PDX1 Protein were considered the highest deleterious mutants. Conclusion: These findings will provide insight into the molecular mechanisms by which PDX1 mutations may contribute to β-cell dysfunction and diabetes development and offer a rational framework for prior-itizing variants for experimental validation and clinical interpretation. Full article

Genetic variation in ATP Binding Cassette Subfamily C Member 6 (ABCC6) can cause both pseudoxanthoma elasticum (PXE) and generalized arterial calcification of infancy (GACI). There are 930 distinct missense variants in ABCC6 reported, 87% of which are of uncertain clinical significance (VUS). New approaches are needed to mechanistically interpret and classify these VUS. We developed 3D protein models of ABCC6 in three functionally relevant conformations to calculate the structural effects of variants. We also used three-dimensional (3D) hotspot detection and developed a mechanistic ontology for critical structure-based functions of ABCC6, enabling us to categorize genomic variants. We identified two 3D hotspots and six specific functions of ABCC6 which variants impact. From this, we propose a mechanism for pathogenicity for 41% of VUS according to their impacted function. We propose that 33 of these variants could be reclassified as Likely Pathogenic with the addition of these structure-based evidence. The mechanistic information we present will guide future research to better address calcification disorders and understand genetic variants. This work emphasizes the benefits of thorough, holistic, and protein-based approaches to genetic interpretation. Further, our VUS reclassification approach will improve the diagnosis of ABCC6-driven diseases, shortening diagnostic odysseys. We believe that computational structural genomics approaches will soon take prominence in genomics data interpretation and variant classification. Full article