Cryogel with Modular and Clickable Building Blocks: Toward the Ultimate Ideal Macroporous Medium for Bacterial Separation.

The lack of practical platforms for bacterial separation remains a hindrance to the detection of bacteria in complex samples. Herein, a composite cryogel was synthesized by using clickable building blocks and boronic acid for bacterial separation. Macroporous cryogels were synthesized by cryo-gelation polymerization using 2-hydroxyethyl methacrylate and allyl glycidyl ether. The interconnected macroporous architecture enabled high interfering substance tolerance. Nanohybrid nanoparticles were prepared via surface-initiated atom transfer radical polymerization and immobilized onto cryogel by click reaction. Alkyne-tagged boronic acid was conjugated to the composite for specific bacteria binding. The physical and chemical characteristics of the composite cryogel were analyzed systematically. Benefitting from the synergistic, multiple binding sites provided by the silica-assisted polymer, the composite cryogel exhibited excellent affinity toward S. aureus and Salmonella spp. with capacities of 91.6 × 107 CFU/g and 241.3 × 107 CFU/g in 0.01 M PBS (pH 8.0), respectively. Bacterial binding can be tuned by variations in pH and temperature and the addition of monosaccharides. The composite was employed to separate S. aureus and Salmonella spp. from spiked tap water, 40% cow milk, and sea cucumber enzymatic hydrolysate, which resulted in high bacteria separation and demonstrated remarkable potential in bacteria separation from food samples.

The density-threshold affinity: Calculating lipid binding affinities from unbiased coarse-grained molecular dynamics simulations.

Many membrane proteins are sensitive to their local lipid environment. As structural methods for membrane proteins have improved, there is growing evidence of direct, specific binding of lipids to protein surfaces. Unfortunately the workhorse of understanding protein-small molecule interactions, the binding affinity for a given site, is experimentally inaccessible for these systems. Coarse-grained molecular dynamics simulations can be used to bridge this gap, and are relatively straightforward to learn. Such simulations allow users to observe spontaneous binding of lipids to membrane proteins and quantify localized densities of individual lipids or lipid fragments. In this chapter we outline a protocol for extracting binding affinities from these localized distributions, known as the "density threshold affinity." The density threshold affinity uses an adaptive and flexible definition of site occupancy that alleviates the need to distinguish between "bound'' lipids and bulk lipids that are simply diffusing through the site. Furthermore, the method allows "bead-level" resolution that is suitable for the case where lipids share binding sites, and circumvents ambiguities about a relevant reference state. This approach provides a convenient and straightforward method for comparing affinities of a single lipid species for multiple sites, multiple lipids for a single site, and/or a single lipid species modeled using multiple forcefields.

Single-Step Purification of Catalase Enzyme From Human Blood Erythrocytes Using Affinity Chromatography Technique.

In this study, we aimed to isolate and purify catalase from human blood erythrocytes by using a newly synthesized affinity gel. The synthesized ω-amino hexyl agarose-1,2,3-triazole-5-carboxylic acid affinity gel was analyzed by FT-IR. Then, different buffer, pH, and ionic strength parameters were optimized to determine the equilibration, washing, and elution buffer conditions. The catalase was purified from human blood erythrocytes with a specific activity of 45.58 EU/mg, purification fold of 529.50, and a yield of 0.416% using the synthesized new affinity gel. The purity and molecular weight of the enzyme were analyzed by SDS-PAGE, and a single band at 60 kDa was observed for catalase. The optimum reaction temperature of the catalase was found to be 30°C, while the thermal stability temperature was 60°C. The Km and Vmax of the enzyme for hydrogen peroxide were calculated at 0.125 mM and 2500 U mL-1, respectively.

Development and validation of extraction and clean-up procedures for UPLC-MS/MS analysis of aflatoxins in spices.

UPLC-MS/MS analytical conditions for the analysis of aflatoxins in spices were optimized and validated in this study. Liquid-liquid partition-based protocols for the cleaning up of extracts using common organic solvents such as acetonitrile, hexane, and ethyl acetate were developed and validated. The developed liquid-liquid partition methods were compared with immuno-affinity column and QuEChERS clean-up methods for the UPLC-MS/MS analysis of aflatoxins in 8 spices. The reduction of lipophilic components using the partition with hexane is particularly useful in spices like red pepper that have higher levels of fatty acids, carotenoids, sterols, triterpenoids, etc. The subsequent partitioning with ethyl acetate considerably reduced the matrix interference from the polar components and increased the sensitivity. The cleaning up of spice extracts using liquid-liquid partition techniques resulted in limits of quantification (LOQ) of 2-5 µgL-1 in UPLC-MS/MS analysis. Trueness, repeatability, and reproducibility of the methods were in acceptable ranges. The accuracy of the developed methods was further verified by analyzing aflatoxins in naturally incurred samples of spices and comparing the results with those obtained from the immuno-affinity column cleanup-HPLC-FD method.

Helicobacter pylori East Asian type CagA hijacks more SHIP2 by its EPIYA-D motif to potentiate the oncogenicity.

CagA is a significant oncogenic factor injected into host cells by Helicobacter pylori, which is divided into two subtypes: East Asian type (CagAE), characterized by the EPIYA-D motif, and western type (CagAW), harboring the EPIYA-C motif. CagAE has been reported to have higher carcinogenicity than CagAW, although the underlying reason is not fully understood. SHIP2 is an intracellular phosphatase that can be recruited by CagA to perturb the homeostasis of intracellular signaling pathways. In this study, we found that SHIP2 contributes to the higher oncogenicity of CagAE. Co-Immunoprecipitation and Pull-down assays showed that CagAE bind more SHIP2 than CagAW. Immunofluorescence staining showed that a higher amount of SHIP2 recruited by CagAE to the plasma membrane catalyzes the conversion of PI(3,4,5)P3 into PI(3,4)P2. This alteration causes higher activation of Akt signaling, which results in enhanced IL-8 secretion, migration, and invasion of the infected cells. SPR analysis showed that this stronger interaction between CagAE and SHIP2 stems from the higher affinity between the EPIYA-D motif of CagAE and the SH2 domain of SHIP2. Structural analysis revealed the crucial role of the Phe residue at the Y + 5 position in EPIYA-D. After mutating Phe of CagAE into Asp (the corresponding residue in the EPIYA-C motif) or Ala, the activation of downstream Akt signaling was reduced and the malignant transformation of infected cells was alleviated. These findings revealed that CagAE hijacks SHIP2 through its EPIYA-D motif to enhance its carcinogenicity, which provides a better understanding of the higher oncogenic risk of H. pylori CagAE.

Diversity, Equity, Inclusion, and Belonging in Veterinary Education.

Interweaving diversity, equity, and inclusion (DEI) into the standards for accreditation requires veterinary schools to review their curriculum and determine what framework works best for them to implement those changes. The Competency-Based Veterinary Education framework is one that is available via the American Association of Veterinary Medical Colleges (AAVMC) to reach those standards. Five standards have DEI components versus having a single standard of DEI as previously Standards of Accreditation by the Council on Education had approved.

GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction.

Accurate prediction of protein-ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric information (i.e. bond angles), leading to difficulties in accurately distinguishing different molecular structures. In addition, these methods also pose limitations in representing the binding process of protein-ligand complexes. To address these issues, we propose a novel geometry-enhanced mid-fusion network, named GEMF, to learn comprehensive molecular geometry and interaction patterns. Specifically, the GEMF consists of a graph embedding layer, a message passing phase, and a multi-scale fusion module. GEMF can effectively represent protein-ligand complexes as graphs, with graph embeddings based on physicochemical and geometric properties. Moreover, our dual-stream message passing framework models both covalent and non-covalent interactions. In particular, the edge-update mechanism, which is based on line graphs, can fuse both distance and angle information in the covalent branch. In addition, the communication branch consisting of multiple heterogeneous interaction modules is developed to learn intricate interaction patterns. Finally, we fuse the multi-scale features from the covalent, non-covalent, and heterogeneous interaction branches. The extensive experimental results on several benchmarks demonstrate the superiority of GEMF compared with other state-of-the-art methods.

Design and characterization of neutral linker-based bis-intercalator via computer simulations: Balancing DNA binding and cellular uptake.

Bis-intercalators play a significant role in altering the DNA structure, affecting its stability, and potentially influencing various cellular processes. These compounds have gained considerable attention in medicinal chemistry and biochemistry due to their potential applications in cancer therapy, where they may interfere with DNA replication and transcription, leading to anticancer effects. Traditionally, these molecules often possess a high positive charge to enhance their affinity for the negatively charged DNA. However, due to a high positive charge, their cellular uptake is compromised, along with their enhanced potential off-target effects. In this study, we utilized bis-intercalator TOTO and replaced the charged linker segment (propane-1,3-diammonium) with a neutral peroxodisulphuric acid linker. Using molecular modeling and computer simulations (500 ns, 3 replicas), we investigated the potential of the designed molecule as a bis-intercalator and compared the properties with the control bis-intercalator bound to DNA. We observed that the designed bis-intercalator exhibited improved DNA binding (as assessed through MM-PBSA and Delphi methods) and membrane translocation permeability. With an overall reduced charge, significantly less off-target binding of the designed molecule is also anticipated. Consequently, bis-intercalators based on peroxodisulphuric linkers can potentially target DNA effectively, and their role in the future design of bis-intercalators is foreseen.

Design and synthesis of novel site-specific antibody-drug conjugates that target TROP2.

The approval of Trodelvy® validates TROP2 as a druggable but challenging target for antibody-drug conjugates (ADCs) to treat metastatic triple-negative breast cancer (mTNBC). Here, based on the TROP2-targeted antibody sacituzumab, we designed and developed several site-specific ADC candidates, which employ MMAE (monomethyl auristatin E) as the toxin, via IgG glycoengineering or affinity-directed traceless conjugation. Systematic evaluation of these site-specific ADCs in homogeneity, hydrophilicity, stability, and antitumor efficiency was conducted. The results indicate that the site-specific ADCs gsADC 3b made from one-step glycoengineering exhibit good aggregation stability and in vivo efficacy, providing a new format of ADCs that target TROP2.

The Immunotherapy of Acute Myeloid Leukemia: A Clinical Point of View.

The potential of the immune system to eradicate leukemic cells has been consistently demonstrated by the Graft vs. Leukemia effect occurring after allo-HSCT and in the context of donor leukocyte infusions. Various immunotherapeutic approaches, ranging from the use of antibodies, antibody-drug conjugates, bispecific T-cell engagers, chimeric antigen receptor (CAR) T-cells, and therapeutic infusions of NK cells, are thus currently being tested with promising, yet conflicting, results. This review will concentrate on various types of immunotherapies in preclinical and clinical development, from the point of view of a clinical hematologist. The most promising therapies for clinical translation are the use of bispecific T-cell engagers and CAR-T cells aimed at lineage-restricted antigens, where overall responses (ORR) ranging from 20 to 40% can be achieved in a small series of heavily pretreated patients affected by refractory or relapsing leukemia. Toxicity consists mainly in the occurrence of cytokine-release syndrome, which is mostly manageable with step-up dosing, the early use of cytokine-blocking agents and corticosteroids, and myelosuppression. Various cytokine-enhanced natural killer products are also being tested, mainly as allogeneic off-the-shelf therapies, with a good tolerability profile and promising results (ORR: 20-37.5% in small trials). The in vivo activation of T lymphocytes and NK cells via the inhibition of their immune checkpoints also yielded interesting, yet limited, results (ORR: 33-59%) but with an increased risk of severe Graft vs. Host disease in transplanted patients. Therefore, there are still several hurdles to overcome before the widespread clinical use of these novel compounds.

ProBASS - a language model with sequence and structural features for predicting the effect of mutations on binding affinity.

Protein-protein interactions (PPIs) govern virtually all cellular processes. Even a single mutation within PPI can significantly influence overall protein functionality and potentially lead to various types of diseases. To date, numerous approaches have emerged for predicting the change in free energy of binding (ΔΔGbind) resulting from mutations, yet the majority of these methods lack precision. In recent years, protein language models (PLMs) have been developed and shown powerful predictive capabilities by leveraging both sequence and structural data from protein-protein complexes. Yet, PLMs have not been optimized specifically for predicting ΔΔGbind. We developed an approach to predict effects of mutations on PPI binding affinity based on two most advanced protein language models ESM2 and ESM-IF1 that incorporate PPI sequence and structural features, respectively. We used the two models to generate embeddings for each PPI mutant and subsequently fine-tuned our model by training on a large dataset of experimental ΔΔGbind values. Our model, ProBASS (Protein Binding Affinity from Structure and Sequence) achieved a correlation with experimental ΔΔGbind values of 0.83 ± 0.05 for single mutations and 0.69 ± 0.04 for double mutations when model training and testing was done on the same PDB. Moreover, ProBASS exhibited very high correlation (0.81 ± 0.02) between prediction and experiment when training and testing was performed on a dataset containing 2325 single mutations in 132 PPIs. ProBASS surpasses the state-of-the-art methods in correlation with experimental data and could be further trained as more experimental data becomes available. Our results demonstrate that the integration of extensive datasets containing ΔΔGbind values across multiple PPIs to refine the pre-trained PLMs represents a successful approach for achieving a precise and broadly applicable model for ΔΔGbind prediction, greatly facilitating future protein engineering and design studies.

Inter domain linker region affects properties of CBM6 in GH5_34 arabinoxylanases and alters oligosaccharide product profile.

Understanding the relation between enzyme domain structure and catalytic activity is crucial for optimal engineering of novel enzymes for lignocellulose bioconversion. Xylanases with varying specificities are commonly used to valorise the hemicellulose arabinoxylan (AX), yet characterization of specific arabinoxylanases remain limited. Two homologous GH5_34 arabinoxylanases, HhXyn5A and CtXyn5A, in which the two domains are connected by a 40-residue linker, exhibit distinct activity on AX, yielding different reaction product patterns, despite high sequence identity, conserved active sites and similar domain composition. In this study, the carbohydrate binding module 6 (CBM6), or the inter domain linker together with CBM6, were swapped to investigate their influence on hydrolytic activity and oligosaccharide product pattern on cereal AXs. The variants, with only CBM6 swapped, displayed reduced activity on commercial wheat and rye AX, as well as on extracted oat fibre, compared to the original enzymes. Additionally, exchange of both linker and CBM6 resulted in a reduced ratio of enzyme produced in soluble form in Escherichia coli cultivations, causing loss of activity of both HhXyn5A and CtXyn5A variants. Analysis of oligosaccharide product patterns applying HPAEC-PAD revealed a decreased number of reaction products for CtXyn5A with swapped CBM6, which resembled the product pattern of HhXyn5A. These findings emphasize the importance of the CBM6 interactions with the linker and the catalytic domain for enzyme activity and specificity, and underlines the role of the linker in enzyme structure organisation and product formation, where alterations in linker interactions with the catalytic and/or CBM6 domains, influence enzyme-substrate association and specificity.

Serine/Threonine Protein Kinases as Attractive Targets for Anti-Cancer Drugs-An Innovative Approach to Ligand Tuning Using Combined Quantum Chemical Calculations, Molecular Docking, Molecular Dynamic Simulations, and Network-like Similarity Graphs.

Serine/threonine protein kinases (CK2, PIM-1, RIO1) are constitutively active, highly conserved, pleiotropic, and multifunctional kinases, which control several signaling pathways and regulate many cellular functions, such as cell activity, survival, proliferation, and apoptosis. Over the past decades, they have gained increasing attention as potential therapeutic targets, ranging from various cancers and neurological, inflammation, and autoimmune disorders to viral diseases, including COVID-19. Despite the accumulation of a vast amount of experimental data, there is still no "recipe" that would facilitate the search for new effective kinase inhibitors. The aim of our study was to develop an effective screening method that would be useful for this purpose. A combination of Density Functional Theory calculations and molecular docking, supplemented with newly developed quantitative methods for the comparison of the binding modes, provided deep insight into the set of desirable properties responsible for their inhibition. The mathematical metrics helped assess the distance between the binding modes, while heatmaps revealed the locations in the ligand that should be modified according to binding site requirements. The Structure-Binding Affinity Index and Structural-Binding Affinity Landscape proposed in this paper helped to measure the extent to which binding affinity is gained or lost in response to a relatively small change in the ligand's structure. The combination of the physico-chemical profile with the aforementioned factors enabled the identification of both "dead" and "promising" search directions. Tests carried out on experimental data have validated and demonstrated the high efficiency of the proposed innovative approach. Our method for quantifying differences between the ligands and their binding capabilities holds promise for guiding future research on new anti-cancer agents.

Recombinant AAV Purification.

Purification of rAAV is a crucial unit operation of the AAV production process. It enables the capture of AAV and removal of contaminants such as host cell proteins, host cell DNA, and other cell culture-related impurities. Here we describe the purification of rAAV produced in insect cells Sf9/rBEV by immuno-affinity capture chromatography. The method is fully scale-amenable unlike other traditional purification methods based on ultracentrifugation. The method reported herein has two main steps: (1) the clarification of cell lysate by depth filtration and (2) the selective capture and single-step purification of AAV via immune-affinity chromatography. This purification method has been successfully implemented to purify the majority of wild-type AAV serotypes.

Bivalent affinity binding-inspired PPARγ immobilization with selective conformation and improved ligand-binding activity.

Functional protein immobilization forms the basis for bio-detections. A series of one-point, site-specific immobilization methods have been developed, however, it still remains as a challenge how to avoid the proteins to move in all directions as well as conveniently regenerate the bio-devices. Herein, we have developed a bivalent affinity binding-inspired method for PPARγ immobilization using DNA aptamer and nickel-nitrilotriacetic acid (Ni2+-NTA) chelation. The specific DNA aptamer (Apt 2) was selected by an on-column systematic evolution of ligands by exponential enrichment (SELEX) method with affinity of (1.57 ± 0.15) × 105 M-1, determined by isothermal titration calorimetry (ITC). Apt 2 and nickel-nitrilotriacetic acid (Ni2+-NTA) were modified on macroporous silica gels via L-α-allylglycine as a linker. They respectively interacted with PPARγ and 6×His tag via bivalent affinity binding for the receptor immobilization. After comprehensive surface characterization, PPARγ was proved to be successful immobilized. Chromatographic studies revealed that the immobilized PPARγ has conformation selectivity, which discriminated agonist and antagonist of the receptor. Ligand-binding parameters (affinity and rate constant) of four agonists (rosiglitazone, pioglitazone, troglitazone, and magnolol) with PPARγ were determined. Troglitazone showed the lowest dissociation rate constant. The binding affinities (3.28 × 107, 1.91 × 106, 2.25 × 107, and 2.43 × 107 M-1) were highly consistent with the data obtained using purified receptor in solution (2.16 × 107, 4.52 × 106, 1.20 × 107, and 1.56 × 107 M-1), offering reliable bio-detection method for PPARγ and its ligands. Due to the biocompatibility of nuclear receptor with DNA, it is conceivable that the bivalent affinity-based method will be a general method for the immobilization of other nuclear receptors, which may provide selective conformation and improved ligand-binding activity for the receptors.

Software for Predicting Binding Free Energy of Protein-Protein Complexes and Their Mutants.

Protein-protein binding affinity prediction is important for understanding complex biochemical pathways and to uncover protein interaction networks. Quantitative estimation of the binding affinity changes caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. The binding free energies of protein-protein complexes can be predicted using several computational tools. This chapter is a summary of software developed for the prediction of binding free energies for protein-protein complexes and their mutants.

Effect of Hb conformational changes on oxygen transport physiology. / Hb构象变化对氧转运生理功能的影响.

Red blood cells (RBCs) are the primary mediators of oxygen transport in the human body, and their function is mainly achieved through conformational changes of hemoglobin (Hb). Hb is a tetramer composed of four subunits, with HbA being the predominant Hb in healthy adults, existing in two forms: tense state (T state) and relaxed state (R state). Endogenous regulators of Hb conformation include 2,3-diphosphoglyceric acid, carbon dioxide, protons, and chloride ions, while exogenous regulators include inositol hexaphosphate, inositol tripyrophosphate, benzabate, urea derivative L35, and vanillin, each with different mechanisms of action. The application of Hb conformational regulators provides new insights into the study of hypoxia oxygen supply issues and the treatment of sickle cell disease.

In-party love spreads more efficiently than out-party hate in online communities.

In this article, we present the findings of a comprehensive longitudinal social network analysis conducted on Twitter across four consecutive election campaigns in Spain, spanning from 2015 to 2019. Our focus is on the discernible trend of increasing partisan and ideological homogeneity within interpersonal exchanges on this social media platform, alongside high levels of networking efficiency measured through average retweeting. This diachronic study allows us to observe how dynamics of party competition might contribute to perpetuating and strengthening network ideological and partisan homophily, creating 'epistemic bubbles' in Twitter, yet showing a greater resistance to transforming them into 'partisan echo-chambers.' Specifically, our analysis reveals that the rise of a new radical right-wing party (RRP), Vox, has heightened ideological homogeneity among users across the entire ideological spectrum. However, this process has not been uniform. While users aligned with mainstream political parties consistently share content that reinforces in-party affinity, resulting in highly efficient 'epistemic bubbles,' the emergence of the RRP has given rise to a distinct group of users associated with the most extreme partisan positions, characterized by a notable proportion of out-partisan hostility content, which has fostered the creation of low-efficient 'partisan echo-chambers.'

Liposome-Papain Conjugates for Catalytic Digestion of Antibody Producing Fab Fragments.

Papain is useful for the enzymatic digestion of various proteins to produce functional peptides or protein fragments. Immobilized papain being reactive toward proteins and easily removable from a reaction mixture is worth developed. In the present work, liposomes were applied as colloidal carriers of papain for the catalytic digestion of polyclonal immunoglobulin G (IgG). Papain was covalently conjugated at pH = 7.0 via tris-succinimidyl aminotriacetate (TSAT) to liposomes incorporated with 5 mol % poly(ethylene glycol)-tethered lipid with a reactive amino group. The papain-conjugated liposome (liposome-papain) catalyzed the hydrolysis of Nα-benzoyl-l-arginine 4-nitroanilide hydrochloride (BAPNA) at pH = 5.0-7.0. The activity of liposome-papain significantly increased with increasing temperature from 25 to 50 °C. The Michaelis constant Km was determined with respect to the liposome-papain- and free papain-catalyzed reactions with BAPNA at 37 °C as Km = 1.11 ± 0.13 and 11.6 ± 2.9 mM, respectively. Liposome-papain was applied to the catalytic digestion of 10 mg·mL-1 IgG at 37 °C for 24 h at pH = 5.0-7.0. The reaction mixture could be analyzed without pretreatment by using the affinity columns immobilized with the protein A or protein L ligand because colloidal liposome-papain quickly flowed through the chromatographic stationary phase, exhibiting little proteolytic effect on the proteinaceous ligands. The analysis clearly demonstrated the catalytic production of antigen-binding fragments (Fab) from IgG in an enzyme concentration- and pH-dependent manner. Liposome-papain with 15 or 50 mol % anionic lipids also catalyzed the formation of Fab from IgG. The above results demonstrated that liposome-papain was useful to digest IgG and to purify Fab formed with the affinity chromatography.

A deep attention model for wide-genome protein-peptide binding affinity prediction at a sequence level.

Peptides are pivotal in numerous biological activities by engaging in up to 40 % of protein-protein interactions in many cellular processes. Due to their exceptional specificity and effectiveness, peptides have emerged as promising candidates for drug design. However, accurately predicting protein-peptide binding affinity remains a challenging. Aiming at the problem, we develop a prediction model PepPAP based on convolutional neural network and multi-head attention, which relies solely on sequence features. These features include physicochemical properties, intrinsic disorder, sequence encoding, and especially interface propensity which is extracted from 16,689 non-redundant protein-peptide complexes. Notably, the adopted regression stratification cross-validation scheme proposed in our previous work is beneficial to improve the prediction for the cases with extreme binding affinity values. On three benchmark test datasets: T100, a series of peptides targeting to PDZ domain and CXCR4, PepPAP shows excellent performance, outperforming the existing methods and demonstrating its good generalization ability. Furthermore, PepPAP has good results in binary interaction prediction, and the analysis of the feature space distribution visualization highlights PepPAP's effectiveness. To the best of our knowledge, PepPAP is the first sequence-based deep attention model for wide-genome protein-peptide binding affinity prediction, and holds the potential to offer valuable insights for the peptide-based drug design.