Structure of the decoy module of human glycoprotein 2 and uromodulin and its interaction with bacterial adhesin FimH

Machine learning in preclinical drug discovery

Drug-discovery and drug-development endeavors are laborious, costly and time consuming. These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of more than 90%. Machine learning (ML) presents an opportunity to improve the drug-discovery process. Indeed, with the growing abundance of public and private large-scale biological and chemical datasets, ML techniques are becoming well positioned as useful tools that can augment the traditional drug-development process. In this Perspective, we discuss the integration of algorithmic methods throughout the preclinical phases of drug discovery. Specifically, we highlight an array of ML-based efforts, across diverse disease areas, to accelerate initial hit discovery, mechanism-of-action (MOA) elucidation and chemical property optimization. With advances in the application of ML across diverse therapeutic areas, we posit that fully ML-integrated drug-discovery pipelines will define the future of drug-development programs.

Uromodulin biology

Uromodulin is a kidney-specific glycoprotein which is exclusively produced by the epithelial cells lining the thick ascending limb and early distal convoluted tubule. It is currently recognized as a multifaceted player in kidney physiology and disease, with discrete roles for intracellular, urinary, interstitial and serum uromodulin. Among these, uromodulin modulates renal sodium handling through the regulation of tubular sodium transporters that reabsorb sodium and are targeted by diuretics, such as the loop diuretic-sensitive Na+-K+-2Cl- cotransporter type 2 (NKCC2) and the thiazide-sensitive Na+/Cl- cotransporter (NCC). Given these roles, the contribution of uromodulin to sodium-sensitive hypertension has been proposed. However, recent studies in humans suggest a more complex interaction between dietary sodium intake, uromodulin and blood pressure. This review presents an updated overview of the uromodulin's biology and its various roles, and focuses on the interaction between uromodulin and sodium-sensitive hypertension.

Protein-based bioactive coatings: from nanoarchitectonics to applications

Protein-based bioactive coatings have emerged as a versatile and promising strategy for enhancing the performance and biocompatibility of diverse biomedical materials and devices. Through surface modification, these coatings confer novel biofunctional attributes, rendering the material highly bioactive. Their widespread adoption across various domains in recent years underscores their importance. This review systematically elucidates the behavior of protein-based bioactive coatings in organisms and expounds on their underlying mechanisms. Furthermore, it highlights notable advancements in artificial synthesis methodologies and their functional applications in vitro. A focal point is the delineation of assembly strategies employed in crafting protein-based bioactive coatings, which provides a guide for their expansion and sustained implementation. Finally, the current trends, challenges, and future directions of protein-based bioactive coatings are discussed.

Glycoprotein 2 as a gut gate keeper for mucosal equilibrium between inflammation and immunity

Glycoprotein 2 (GP2) is a widely distributed protein in the digestive tract, contributing to mucosal barrier maintenance, immune homeostasis, and antigen-specific immune response, while also being linked to inflammatory bowel disease (IBD) pathogenesis. This review sheds light on the extensive distribution of GP2 within the gastrointestinal tract and its intricate interplay with the immune system. Furthermore, the significance of GP2 autoantibodies in diagnosing and categorizing IBD is underscored, alongside the promising therapeutic avenues for modulating GP2 to regulate immunity and maintain mucosal balance.

Allelic effects on uromodulin aggregates drive autosomal dominant tubulointerstitial kidney disease

Missense mutations in the uromodulin (UMOD) gene cause autosomal dominant tubulointerstitial kidney disease (ADTKD), one of the most common monogenic kidney diseases. The unknown impact of the allelic and gene dosage effects and fate of mutant uromodulin leaves open the gap between postulated gain-of-function mutations, end-organ damage and disease progression in ADTKD. Based on two prevalent missense UMOD mutations with divergent disease progression, we generated UmodC171Y and UmodR186S knock-in mice that showed strong allelic and gene dosage effects on uromodulin aggregates and activation of ER stress and unfolded protein and immune responses, leading to variable kidney damage. Deletion of the wild-type Umod allele in heterozygous UmodR186S mice increased the formation of uromodulin aggregates and ER stress. Studies in kidney tubular cells confirmed differences in uromodulin aggregates, with activation of mutation-specific quality control and clearance mechanisms. Enhancement of autophagy by starvation and mTORC1 inhibition decreased uromodulin aggregates. These studies substantiate the role of toxic aggregates as driving progression of ADTKD-UMOD, relevant for therapeutic strategies to improve clearance of mutant uromodulin.

Biological function, regulatory mechanism, and clinical application of mannose in cancer

Studies examining the regulatory roles and clinical applications of monosaccharides other than glucose in cancer have been neglected. Mannose, a common type of monosaccharide found in human body fluids and tissues, primarily functions in protein glycosylation rather than carbohydrate metabolism. Recent research has demonstrated direct anticancer effects of mannose in vitro and in vivo. Simply supplementing cell culture medium or drinking water with mannose achieved these effects. Moreover, mannose enhances the effectiveness of current cancer treatments including chemotherapy, radiotherapy, targeted therapy, and immune therapy. Besides the advancements in basic research on the anticancer effects of mannose, recent studies have reported its application as a biomarker for cancer or in the delivery of anticancer drugs using mannose-modified drug delivery systems. This review discusses the progress made in understanding the regulatory roles of mannose in cancer progression, the mechanisms underlying its anticancer effects, and its current application in cancer diagnosis and treatment.

Characterization of site-specific N-glycosylation signatures of isolated uromodulin from human urine

Uromodulin (Umod, Tamm-Horsfall protein) is the most abundant urinary N-glycoprotein produced exclusively by the kidney. It can form filaments to antagonize the adhesion of uropathogens. However, the site-specific N-glycosylation signatures of Umod in healthy individuals and patients with IgA nephropathy (IgAN) remain poorly understood due to the lack of suitable isolation and analytical methods. In this study, we first presented a simple and fast method based on diatomaceous earth adsorption to isolate Umod. These isolated glycoproteins were digested by trypsin and/or Glu-C. Intact N-glycopeptides with or without HILIC enrichment were analyzed using our developed EThcD-sceHCD-MS/MS. Based on the optimized workflow, we identified a total of 780 unique intact N-glycopeptides (7 N-glycosites and 152 N-glycan compositions) from healthy individuals. As anticipated, these glycosites exhibited glycoform heterogeneity. Almost all N-glycosites were modified completely by the complex type, except for one N-glycosite (N275), which was nearly entirely occupied by the high-mannose type for mediating Umod's antiadhesive activity. Then, we compared the N-glycosylation of Umod between healthy controls (n = 9) and IgAN patients (n = 9). The N-glycosylation of Umod in IgAN patients will drastically decrease and be lost. Finally, we profiled the most comprehensive site-specific N-glycosylation map of Umod and revealed its alterations in IgAN patients. Our method provides a high-throughput workflow for characterizing the N-glycosylation of Umod, which can aid in understanding its roles in physiology and pathology, as well as serving as a potential diagnostic tool for evolution of renal tubular function.

Structural insights into a cooperative switch between one and two FimH bacterial adhesins binding pauci- and high-mannose type N-glycan receptors

The FimH type-1 fimbrial adhesin allows pathogenic Escherichia coli to adhere to glycoproteins in the epithelial linings of human bladder and intestinal tract, by using multiple fimbriae simultaneously. Pauci- and high-mannose type N-glycans are natural FimH receptors on those glycoproteins. Oligomannose-3 and -5 bind with the highest affinity to FimH by using the same Manα1,3Man branch. Oligomannose-6 is generated from oligomannose-5 in the next step of the biogenesis of high-mannose N-glycans, by the transfer of a mannose in α1,2-linkage onto this branch. Using serial crystallography and by measuring the kinetics of binding, we demonstrate that shielding the high-affinity epitope drives the binding of multiple FimH molecules. First, we profiled FimH glycan binding on a microarray containing paucimannosidic N-glycans and in a FimH LEctPROFILE® assay. To make the transition to oligomannose-6, we measured the kinetics of FimH binding using paucimannosidic N-glycans, glycoproteins and all four α-dimannosides conjugated to bovine serum albumin. Equimolar mixed interfaces of the dimannosides present in oligomannose-6 and molecular dynamics simulations suggest a positive cooperativity in the bivalent binding of Manα1,3Manα1 and Manα1,6Manα1 dimannosides. The binding of core α1,6-fucosylated oligomannose-3 in the co-crystals of FimH is monovalent, but interestingly the GlcNAc1 - Fuc moiety retains highly flexibility. In co-crystals with oligomannose-6, two FimH bacterial adhesins bind the Manα1,3Manα1 and Manα1,6Manα1 endings of the second trimannose core (A-4'-B). This cooperative switch towards bivalent binding appears sustainable beyond a molar excess of oligomannose-6. Our findings provide important novel structural insights for the design of multivalent FimH antagonists that bind with positive cooperativity.

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

Accelerating crystal structure determination with iterative AlphaFold prediction

Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of C^α atoms matching those in the deposited models within 2 Å. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested.

Exploring AlphaFold2's Performance on Predicting Amino Acid Side-Chain Conformations and Its Utility in Crystal Structure Determination of B318L Protein

Recent technological breakthroughs in machine-learning-based AlphaFold2 (AF2) are pushing the prediction accuracy of protein structures to an unprecedented level that is on par with experimental structural quality. Despite its outstanding structural modeling capability, further experimental validations and performance assessments of AF2 predictions are still required, thus necessitating the development of integrative structural biology in synergy with both computational and experimental methods. Focusing on the B318L protein that plays an essential role in the African swine fever virus (ASFV) for viral replication, we experimentally demonstrate the high quality of the AF2 predicted model and its practical utility in crystal structural determination. Structural alignment implies that the AF2 model shares nearly the same atomic arrangement as the B318L crystal structure except for some flexible and disordered regions. More importantly, side-chain-based analysis at the individual residue level reveals that AF2's performance is likely dependent on the specific amino acid type and that hydrophobic residues tend to be more accurately predicted by AF2 than hydrophilic residues. Quantitative per-residue RMSD comparisons and further molecular replacement trials suggest that AF2 has a large potential to outperform other computational modeling methods in terms of structural determination. Additionally, it is numerically confirmed that the AF2 model is accurate enough so that it may well potentially withstand experimental data quality to a large extent for structural determination. Finally, an overall structural analysis and molecular docking simulation of the B318L protein are performed. Taken together, our study not only provides new insights into AF2's performance in predicting side-chain conformations but also sheds light upon the significance of AF2 in promoting crystal structural determination, especially when the experimental data quality of the protein crystal is poor.

Systemic Effects of Tamm-Horsfall Protein in Kidney Disease

Tamm-Horsfall protein (THP) is produced exclusively by the kidney, where it is released into both the urine and the circulation. Although the primary form of circulating THP is nonpolymerizing, urinary THP exists as a mix of polymerizing and nonpolymerizing forms. Urinary THP has been shown to play roles in such disparate processes as prevention of urinary tract infections and kidney stone formation, along with the regulation of multiple ion channels within the kidney. The generation of THP knockout mouse models has allowed the investigation of these phenomena and shown a prospective role for circulating THP in ischemia-reperfusion acute kidney injury as well as sepsis. Recent studies have suggested that THP is protective in ischemic injury owing to its inhibition of oxidative stress via the calcium channel transient receptor potential cation channel, subfamily M, member 2 t(TRPM2), and protection in sepsis is at least partially due to THP's promotion of macrophage function.