Search Results (15)

Hypervirulent Klebsiella pneumoniae (hvKp) has emerged as a significant public health concern, yet rare sublineages remain poorly characterized. Here, we described a K30-ST198 hvKp sublineage identified in four isolates from two patients, including three sequential strains (K30B1, K30B2, K30B3) recovered over eight months from recurrent liver abscesses and one strain (K30-HUMV1) from a urinary tract infection. All isolates exhibited a yYpermucoviscous phenotype and resistance restricted to ampicillin and amoxicillin. Screening with the eazyplex hvKp assay detected ybt and rmpA in all strains, yielding a virulence score of 1. Biofilm production was strong in K30B1, K30B2, moderate in K30-HUMV1, but weak in K30B3. In the Galleria mellonella infection model, K30B1 showed higher virulence than the other isolates. Whole-genome sequencing identified the ICEKp1 carrying hypervirulence-associated genes (ybt, pagO, rmpAC, iroBCDN) together with additional virulence factors (fim, mrkD, uge, ureA, wabG, wcaJ, mliC), while antibiotic resistance genes were limited to fosA and bla_SHV-77. Protein structures and their functional domains were predicted using AlphaFold v3.0.1 and ColabFold v1.5.5, based on pLDDT scores, providing further insights into gene functionality. This work represents one of the first detailed characterizations of K30-ST198 hvKp, underscoring the need for integrated genomic, phenotypic, and structural approaches in hvKp surveillance. Full article

Cryo-electron microscopy single-particle analysis (cryo-EM SPA) has advanced three-dimensional protein structure determination, yet resolving intrinsically disordered proteins and regions (IDPs/IDRs) remains challenging due to conformational heterogeneity. This research evaluates cryo-EM’s capacity to map dynamic regions, assesses the adaptability of disorder prediction tools, and explores optimization strategies for dynamic structure prediction. Cryo-EM SPA datasets from 2000 to 2024 were categorized into different periods, forming a database integrating sequence data and disorder indices. Established prediction tools—AlphaFold2 (pLDDT), flDPnn, and IUPred—were evaluated for transferability, while a multi-level CLTC hybrid model (combining CNN, LSTM, Transformer, and CRF architectures) was developed to link local conformational fluctuations with global sequence contexts. Analyses revealed consistent advancements in average resolution and model counts over the past decade, although mapping disordered regions remained technically demanding. Both the adapted AlphaFold pLDDT and the CLTC model demonstrated efficacy in predicting structurally variable and poorly resolved regions. A subset of the cryo-EM missing residues exhibited intermediate conformational features, suggesting classification ambiguities potentially influenced by experimental conditions. These findings systematically outline the evolving capabilities of cryo-EM in resolving dynamic regions, benchmark the adaptability of computational tools, and introduce a hybrid model to enhance prediction accuracy. This study provides a framework for addressing conformational heterogeneity, contributing to methodological advancements in structural biology. Full article

Phosphomannomutase 2 (PMM2) catalyzes the interconversion of mannose-6-phosphate and mannose-1-phosphate, a key step in the biosynthesis of GDP-mannose for N-glycosylation. Its deficiency is the most common cause of congenital disorders of glycosylation (CDGs), accounting for the subtype known as PMM2-CDG. PMM2-CDG is a rare autosomal recessive disease characterized by multisystemic dysfunction, including cerebellar atrophy, peripheral neuropathy, developmental delay, and coagulation abnormalities. The disease is associated with a spectrum of pathogenic missense mutations, particularly at residues involved in dimerization and catalytic function (i.e., p.Phe119Leu and p.Arg141His). The dimerization of PMM2 is considered essential for enzymatic activity, although it remains unclear whether this supports structural stability alone, or whether both subunits are catalytically active—a distinction that may affect how mutations in each monomer contribute to overall enzyme function and disease phenotype. PMM2 has a paralog, phosphomannomutase 1 (PMM1), which shares substantial structural similarity—including obligate dimerization—and displays mutase activity in vitro, but does not compensate for PMM2 deficiency in vivo. To investigate potential heterodimerization between PMM1 and PMM2 and the effect of interface mutations over PMM2 dimer stability, we first assessed the likelihood of their co-expression using data from GTEx and the Human Protein Atlas. Building on this expression evidence, we modeled all possible dimeric combinations between the two paralogs using AlphaFold3. Models of the PMM2 and PMM1 homodimers were used as internal controls and aligned closely with their respective reference biological assemblies (RMSD < 1 Å). In contrast, the PMM2/PMM1 heterodimer model, the primary result of interest, showed high overall confidence (pLDDT > 90), a low inter-chain predicted alignment error (PAE∼1 Å), and robust interface confidence scores (iPTM = 0.80). Then, we applied PISA, PRODIGY, and mmCSM-PPI to assess interface energetics and evaluate the impact of missense variants specifically at the dimerization interface. Structural modeling suggested that PMM2/PMM1 heterodimers were energetically viable, although slightly less stable than PMM2 homodimers. Interface mutations were predicted to reduce dimer stability, potentially contributing to the destabilizing effects of disease-associated variants. These findings offer a structural framework for understanding PMM2 dimerization, highlighting the role of interface stability, paralogs co-expression, and sensitivity to disease-associated mutations. Full article

This study presents PolypeptideDesigner (PPD), a novel conditional diffusion-based model for de novo polypeptide sequence design and generation based on per-residue secondary structure conditions. By integrating a lightweight LSTM-attention neural network as the denoiser within a diffusion framework, PPD offers an innovative and efficient approach to polypeptide generation. Evaluations demonstrate that the PPD model can generate diverse and novel polypeptide sequences across various testing conditions, achieving high pLDDT scores when folded by ESMFold. In comparison to the ProteinDiffusionGenerator B (PDG-B) model, a relevant benchmark in the field, PPD exhibits the ability to produce longer and more diverse polypeptide sequences. This improvement is attributed to PPD’s optimized architecture and expanded training dataset, which enhance its understanding of protein structural pattern. The PPD model shows significant potential for optimizing functional polypeptides with known structures, paving the way for advancements in biomaterial design. Future work will focus on further refining the model and exploring its broader applications in polypeptide engineering. Full article

Anthocyanins are bioactive compounds responsible for various physiological processes in plants and provide characteristic colors to fruits and flowers. Their biosynthetic pathway is well understood; however, the enzymatic degradation mechanism is less explored. Anthocyanase (β-glucosidase (BGL)), peroxidase (POD), and polyphenol oxidase (PPO) are enzymes involved in degrading anthocyanins in plants such as petunias, eggplants, and Sicilian oranges. The aim of this work was to investigate the physicochemical interactions between these enzymes and the identified anthocyanins (via UPLC-MS/MS) in cranberry (Vaccinium macrocarpon) through molecular docking to identify the residues likely involved in anthocyanin degradation. Three-dimensional models were constructed using the AlphaFold2 server based on consensus sequences specific to each enzyme. The models with the highest confidence scores (pLDDT) were selected, with BGL, POD, and PPO achieving scores of 87.6, 94.8, and 84.1, respectively. These models were then refined using molecular dynamics for 100 ns. Additionally, UPLC-MS/MS analysis identified various flavonoids in cranberries, including cyanidin, delphinidin, procyanidin B2 and B4, petunidin, pelargonidin, peonidin, and malvidin, providing important experimental data to support the study. Molecular docking simulations revealed the most stable interactions between anthocyanase and the anthocyanins cyanidin 3-arabinoside and cyanidin 3-glucoside, with a favorable ΔG of interaction between −9.3 and −9.2 kcal/mol. This study contributes to proposing a degradation mechanism and seeking inhibitors to prevent fruit discoloration. Full article

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in the field of computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough in structural biology has been established by developing the artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 system provides a state-of-the-art prediction of protein structures from nearly all known protein sequences with high accuracy. This study examined the reliability of AF2 models compared to the experimental structures in drug discovery, focusing on one of the most common protein drug-targeted classes known as G protein-coupled receptors (GPCRs) class A. A total of 32 representative protein targets were selected, including experimental structures of X-ray crystallographic and Cryo-EM structures and their corresponding AF2 models. The quality of AF2 models was assessed using different structure validation tools, including the pLDDT score, RMSD value, MolProbity score, percentage of Ramachandran favored, QMEAN Z-score, and QMEANDisCo Global. The molecular docking was performed using the Genetic Optimization for Ligand Docking (GOLD) software. The AF2 models’ reliability in virtual drug screening was determined by their ability to predict the ligand binding poses closest to the native binding pose by assessing the Root Mean Square Deviation (RMSD) metric and docking scoring function. The quality of the docking and scoring function was evaluated using the enrichment factor (EF). Furthermore, the capability of using AF2 models in molecular docking to identify hits with key protein–ligand interactions was analyzed. The posing power results showed that the AF2 models successfully predicted ligand binding poses (RMSD < 2 Å). However, they exhibited lower screening power, with average EF values of 2.24, 2.42, and 1.82 for X-ray, Cryo-EM, and AF2 structures, respectively. Moreover, our study revealed that molecular docking using AF2 models can identify competitive inhibitors. In conclusion, this study found that AF2 models provided docking results comparable to experimental structures, particularly for certain GPCR targets, and could potentially significantly impact drug discovery. Full article

In this study, we advance our exploration of Apolipoprotein A-I (apoA-I) peptide analogs (APAs) for their application in nanodisc (ND) assembly, focusing on the dynamic conformational characteristics and the potential for drug delivery. We explore APA-ND interactions with an emphasis on curcumin encapsulation, utilizing molecular dynamic simulations and in vitro assessments to evaluate the efficacy of various APA-ND formulations as drug carriers. The methodological approach involved the generation of three unique apoA-I α-11/3 helical mimics, resulting in fifteen distinct APAs. Their structural integrity was rigorously assessed using ColabFold-AF2, with particular attention to pLDDT and pTM scores. Extensive molecular dynamics simulations, covering 1.7 μs across 17 ND systems, were conducted to investigate the influence of APA sequence variations on ND stability and interactions. This study reveals that the composition of APAs, notably the presence of Proline, Serine, and Tryptophan, significantly impacts ND stability and morphology. Oligomeric APAs, in particular, demonstrated superior stability and distinct interaction patterns compared to their monomeric counterparts. Additionally, hydrodynamic diameter measurements over eight weeks indicated sequence-dependent stability, highlighting the potential of specific APA configurations for sustained colloidal stability. In vitro study successfully encapsulated curcumin in [AA]₃/DMPC ND formulations, revealing concentration-dependent stability and interaction dynamics. The findings underscore the remarkable capability of APA-NDs to maintain structural integrity and efficient drug encapsulation, positioning them as a promising platform for drug delivery. The study concludes by emphasizing the tunability and versatility of APA-NDs in drug formulation, potentially revolutionizing nanomedicine by enabling customized APA sequences and ND properties for targeted drug delivery. Full article

Two non-redundant, high-quality sets of protein X-ray crystal structures from the Protein Data Bank (room temperature, 288–298 K, and low temperature, 95–105 K) were compared to structural predictions conducted using ColabFold/AlphaFold2. In particular, the relationship between B-factors and pLDDT values, which estimate the degree of prediction confidence, was investigated. It was observed that there is basically no correlation between these two quantities and, consequently, that the level of confidence in predictions does not provide information about the degree of local structural flexibility of globular proteins. Full article

Disorder prediction methods that can discriminate between ordered and disordered regions have contributed fundamentally to our understanding of the properties and prevalence of intrinsically disordered proteins (IDPs) in proteomes as well as their functional roles. However, a recent large-scale assessment of the performance of these methods indicated that there is still room for further improvements, necessitating novel approaches to understand the strengths and weaknesses of individual methods. In this study, we compared two methods, IUPred and disorder prediction, based on the pLDDT scores derived from AlphaFold2 (AF2) models. We evaluated these methods using a dataset from the DisProt database, consisting of experimentally characterized disordered regions and subsets associated with diverse experimental methods and functions. IUPred and AF2 provided consistent predictions in 79% of cases for long disordered regions; however, for 15% of these cases, they both suggested order in disagreement with annotations. These discrepancies arose primarily due to weak experimental support, the presence of intermediate states, or context-dependent behavior, such as binding-induced transitions. Furthermore, AF2 tended to predict helical regions with high pLDDT scores within disordered segments, while IUPred had limitations in identifying linker regions. These results provide valuable insights into the inherent limitations and potential biases of disorder prediction methods. Full article

A novel bacterium, Arthrobacter globiformis DC-1, capable of degrading DDT as its sole carbon and energy source, was isolated from DDT-contaminated agricultural soil. The bacterium can degrade up to 76.3% of the DDT at a concentration of 10 mg/L in the mineral salt medium (MSM) within 1 day of incubation. The effects of various environmental conditions, such as the concentration of DDT, temperature, pH and additional carbon sources, on its growth and biodegrading capacity of DDT were investigated in the MSM. The A. globiformis DC-1 strain could efficiently grow and degrade DDT at a wide range of concentrations, with the maximum growth and degradation rate at 10 mg/LDDT, followed by inhibitory effects at higher concentrations (20 and 30 mg/LDDT). Mesophilic temperatures (25–30 °C) and a pH of 7–7.5 were the most suitable conditions for the growth and biodegradation. The presence of carbon sources significantly increased the growth of the DC-1 strain; however, degradation was inhibited in the present of glucose, sucrose and fructose, and peptone was determined to be the most appropriate carbon source for A. globiformis DC-1. The optimal DDT degradation (84.2%) was observed at 10 mg/LDDT, peptone as carbon source in pH 7.5 at 30 °C with 1 day of incubation. This strain could also degrade DDE, DDD and DDT simultaneously as the sole carbon and energy source, with degradation rates reaching 70.61%, 64.43% and 60.24% in 10 days, respectively. The biodegradation pathway by A. globiformis DC-1 revealed that DDT was converted to DDD and DDE via dechlorination and dehydrochlorination, respectively; subsequently, both DDD and DDE transformed to DDMU through further dechlorination, and finally, after ring opening, DDMU was mineralized to carbon dioxide. No intermediate metabolites accumulation was observed during the GC/MS analysis, demonstrating that the A. globiformis DC-1 strain can be used for the bioremediation of DDT residues in the environment. Full article

During the outbreak of COVID-19, many SARS-CoV-2 variants presented key amino acid mutations that influenced their binding abilities with angiotensin-converting enzyme 2 (hACE2) and neutralizing antibodies. For the B.1.617 lineage, there had been fears that two key mutations, i.e., L452R and E484Q, would have additive effects on the evasion of neutralizing antibodies. In this paper, we systematically investigated the impact of the L452R and E484Q mutations on the structure and binding behavior of B.1.617.1 using deep learning AlphaFold2, molecular docking and dynamics simulation. We firstly predicted and verified the structure of the S protein containing L452R and E484Q mutations via the AlphaFold2-calculated pLDDT value and compared it with the experimental structure. Next, a molecular simulation was performed to reveal the structural and interaction stabilities of the S protein of the double mutant variant with hACE2. We found that the double mutations, L452R and E484Q, could lead to a decrease in hydrogen bonds and higher interaction energy between the S protein and hACE2, demonstrating the lower structural stability and the worse binding affinity in the long dynamic evolutional process, even though the molecular docking showed the lower binding energy score of the S1 RBD of the double mutant variant with hACE2 than that of the wild type (WT) with hACE2. In addition, docking to three approved neutralizing monoclonal antibodies (mAbs) showed a reduced binding affinity of the double mutant variant, suggesting a lower neutralization ability of the mAbs against the double mutant variant. Our study helps lay the foundation for further SARS-CoV-2 studies and provides bioinformatics and computational insights into how the double mutations lead to immune evasion, which could offer guidance for subsequent biomedical studies. Full article

AlphaFold2 (AF2) is a protein structure prediction program which provides accurate models. In addition to predicting structural domains, AF2 assigns intrinsically disordered regions (IDRs) by identifying regions with low prediction reliability (pLDDT). Some regions in IDRs undergo disorder-to-order transition upon binding the interaction partner. Here we assessed model structures of AF2 based on the annotations in IDEAL, in which segments with disorder-to-order transition have been collected as Protean Segments (ProSs). We non-redundantly selected ProSs from IDEAL and classified them based on the root mean square deviation to the corresponding region of AF2 models. Statistical analysis identified 11 structural and sequential features, possibly contributing toward the prediction of ProS structures. These features were categorized into two groups: one that contained pLDDT and the other that contained normalized radius of gyration. The typical ProS structures in the former group comprise a long α helix or a whole or part of the structural domain and those in the latter group comprise a short α helix with terminal loops. Full article

AlphaFold2 (AF2) has created a breakthrough in biology by providing three-dimensional structure models for whole-proteome sequences, with unprecedented levels of accuracy. In addition, the AF2 pLDDT score, related to the model confidence, has been shown to provide a good measure of residue-wise disorder. Here, we combined AF2 predictions with pyHCA, a tool we previously developed to identify foldable segments and estimate their order/disorder ratio, from a single protein sequence. We focused our analysis on the AF2 predictions available for 21 reference proteomes (AFDB v1), in particular on their long foldable segments (>30 amino acids) that exhibit characteristics of soluble domains, as estimated by pyHCA. Among these segments, we provided a global analysis of those with very low pLDDT values along their entire length and compared their characteristics to those of segments with very high pLDDT values. We highlighted cases containing conditional order, as well as cases that could form well-folded structures but escape the AF2 prediction due to a shallow multiple sequence alignment and/or undocumented structure or fold. AF2 and pyHCA can therefore be advantageously combined to unravel cryptic structural features in whole proteomes and to refine predictions for different flavors of disorder. Full article

A whole-exome capture and next-generation sequencing was applied to an 11 y/o patient with a clinical history of congenital hypotonia, generalized motor and cognitive neurodevelopmental delay, and severe cognitive deficit, and without any identifiable Syndromic pattern, and to her parents, we disclosed a de novo heterozygous pathogenic mutation, c.697_699del p.Phe233del (rs786204835)(ACMG classification PS2, PM1, PM2, PP5), harbored in the PURA gene (MIM*600473) (5q31.3), associated with Autosomal Dominant Mental Retardation 31 (MIM # 616158). We used the significant improvement in the accuracy of protein structure prediction recently implemented in AlphaFold that incorporates novel neural network architectures and training procedures based on the evolutionary, physical, and geometric constraints of protein structures. The wild-type (WT) sequence and the mutated sequence, missing the Phe233, were reconstructed. The predicted local Distance Difference Test (lDDT) for the PURAwt and the PURA–Phe233del showed that the occurrence of the Phe233del affects between 220–320 amino acids. The distortion in the PURA structural conformation in the ~5 Å surrounding area after the p.Phe233del produces a conspicuous disruption of the repeat III, where the DNA and RNA helix unwinding capability occurs. PURA Protein–DNA docking corroborated these results in an in silico analysis that showed a loss of the contact of the PURA–Phe233del III repeat domain model with the DNA. Together, (i) the energetic and stereochemical, (ii) the hydropathic indexes and polarity surfaces, and (iii) the hybrid Quantum Mechanics–Molecular Mechanics (QM–MM) analyses of the PURA molecular models demarcate, at the atomic resolution, the specific surrounding region affected by these mutations and pave the way for future cell-based functional analysis. To the best of our knowledge, this is the first report of a de novo mutation underpinning a PURA syndrome in a Latin American patient and highlights the importance of predicting the molecular effects in protein structure using artificial intelligence algorithms and molecular and atomic resolution stereochemical analyses. Full article

The development of AlphaFold2 marked a paradigm-shift in the structural biology community. Herein, we assess the ability of AlphaFold2 to predict disordered regions against traditional sequence-based disorder predictors. We find that AlphaFold2 performs well at discriminating disordered regions, but also note that the disorder predictor one constructs from an AlphaFold2 structure determines accuracy. In particular, a naïve, but non-trivial assumption that residues assigned to helices, strands, and H-bond stabilized turns are likely ordered and all other residues are disordered results in a dramatic overestimation in disorder; conversely, the predicted local distance difference test (pLDDT) provides an excellent measure of residue-wise disorder. Furthermore, by employing molecular dynamics (MD) simulations, we note an interesting relationship between the pLDDT and secondary structure, that may explain our observations and suggests a broader application of the pLDDT for characterizing the local dynamics of intrinsically disordered proteins and regions (IDPs/IDRs). Full article