Virtual Screening Against Carbohydrate-Binding Proteins: Evaluation and Application to Bacterial Burkholderia ambifaria Lectin

Hit Identification Driven by Combining Artificial Intelligence and Computational Chemistry Methods: A PI5P4K-β Case Study

Computer-aided drug design (CADD), especially artificial intelligence-driven drug design (AIDD), is increasingly used in drug discovery. In this paper, a novel and efficient workflow for hit identification was developed within the ID4Inno drug discovery platform, featuring innovative artificial intelligence, high-accuracy computational chemistry, and high-performance cloud computing. The workflow was validated by discovering a few potent hit compounds (best IC50 is ∼0.80 μM) against PI5P4K-β, a novel anti-cancer target. Furthermore, by applying the tools implemented in ID4Inno, we managed to optimize these hit compounds and finally obtained five hit series with different scaffolds, all of which showed high activity against PI5P4K-β. These results demonstrate the effectiveness of ID4inno in driving hit identification based on artificial intelligence, computational chemistry, and cloud computing.

Glycomimetics for the inhibition and modulation of lectins

Carbohydrates are essential mediators of many processes in health and disease. They regulate self-/non-self- discrimination, are key elements of cellular communication, cancer, infection and inflammation, and determine protein folding, function and life-times. Moreover, they are integral to the cellular envelope for microorganisms and participate in biofilm formation. These diverse functions of carbohydrates are mediated by carbohydrate-binding proteins, lectins, and the more the knowledge about the biology of these proteins is advancing, the more interfering with carbohydrate recognition becomes a viable option for the development of novel therapeutics. In this respect, small molecules mimicking this recognition process become more and more available either as tools for fostering our basic understanding of glycobiology or as therapeutics. In this review, we outline the general design principles of glycomimetic inhibitors (Section 2). This section is then followed by highlighting three approaches to interfere with lectin function, i.e. with carbohydrate-derived glycomimetics (Section 3.1), novel glycomimetic scaffolds (Section 3.2) and allosteric modulators (Section 3.3). We summarize recent advances in design and application of glycomimetics for various classes of lectins of mammalian, viral and bacterial origin. Besides highlighting design principles in general, we showcase defined cases in which glycomimetics have been advanced to clinical trials or marketed. Additionally, emerging applications of glycomimetics for targeted protein degradation and targeted delivery purposes are reviewed in Section 4.

Multifaceted Computational Modeling in Glycoscience

Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate-carbohydrate interactions, glycolipids, and N- and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics.

Druggable Allosteric Sites in β-Propeller Lectins

Carbohydrate‐binding proteins (lectins) are auspicious targets in drug discovery to combat antimicrobial resistance; however, their non‐carbohydrate drug‐like inhibitors are still unavailable. Here, we present a druggable pocket in a β‐propeller lectin BambL from Burkholderia ambifaria as a potential target for allosteric inhibitors. This site was identified employing ¹⁹F NMR fragment screening and a computational pocket prediction algorithm SiteMap. The structure–activity relationship study revealed the most promising fragment with a dissociation constant of 0.3±0.1 mM and a ligand efficiency of 0.3 kcal mol⁻¹ HA⁻¹ that affected the orthosteric site. This effect was substantiated by site‐directed mutagenesis in the orthosteric and secondary pockets. Future drug‐discovery campaigns that aim to develop small molecule inhibitors can benefit from allosteric sites in lectins as a new therapeutic approach against antibiotic‐resistant pathogens.

Biofilm-Integrated Glycosylated Membrane for Biosuccinic Acid Production

Biofilm-based cell-immobilized fermentation technology is regarded as the technique with the most potential for biobased product (chemicals, biofuelss materials, etc.) production in industry. Glycosylated membrane can mimic natural extracellular matrix (ECM) and improve cell adhesion and biofilm formation based on carbohydrate-microbial lectin interaction. Here, we applied glycosylated membrane with rhamnose modified surface for constructing Actinobacillus succinogenes biofilm and producing biosuccinic acid. Polymer hollow fiber (PHF) membrane surface was first modified by glycosylation based on physical adsorption approach. The approach is simple, green, and suitable for scale-amplification. Then, the microbial biofilm formed dramatically on the modified membrane surface. And for subsequent biosuccinic acid production, the maximum titer of succinic acid reached 67.3 g/L, and the yield was 0.82 g/g. Compared with free cell fermentation, the titer and yield increased by 18% and 9% in this biofilm-based cell-immobilized fermentation system, respectively. Importantly, the production efficiency of biosuccinic acid increased obviously for subsequent biofilm-based cell-immobilized fermentation. In addition, the biofilm-integrated glycosylated membrane showed high reusability for succinic acid production. This result is important for developing biofilms for a wide range of applications in bioproduct (chemicals, biofuels, materials, etc.) production.

Rigorous Free Energy Simulations in Virtual Screening

Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows. In this Perspective, we assert that alchemical binding free energy methods using all-atom molecular dynamics simulations have matured to the point where they can be applied in virtual screening campaigns as a final scoring stage to prioritize the top molecules for experimental testing. Specifically, we propose that alchemical absolute binding free energy (ABFE) calculations offer the most direct and computationally efficient approach within a rigorous statistical thermodynamic framework for computing binding energies of diverse molecules, as is required for virtual screening. ABFE calculations are particularly attractive for drug discovery at this point in time, where the confluence of large-scale genomics data and insights from chemical biology have unveiled a large number of promising disease targets for which no small molecule binders are known, precluding ligand-based approaches, and where traditional docking approaches have foundered to find progressible chemical matter.